In recent years, information technologies have been widely discussed in the literature for their contribution to more environmentally responsible human activities (Baggia et al., 2019; Dam et al., 2024). Isensee et al. (2020) noted that environmental sustainability and digitalization have a positive effect on the evolution of small and medium-sized enterprises (SMEs). Digitalization is defined as a “tool” that transforms interactions, communications, business functions, and business models into digital environments (I-Scoop, 2021).

The global economic crisis, competitors’ strategies, and governmental changes are among the factors that promote the adoption of green technologies (Buchalcevova & Gala, 2013). Cloud-based tools are particularly relevant in this context because they do not require substantial investments in technological infrastructure. Consequently, Yiu et al. (2021) proposed the daily use of cloud-embedded business intelligence systems as a strategic initiative to enhance a firm’s resources by adding value to information and aligning objectives with organizational strategies. From this perspective, business intelligence systems can be incorporated into production and operations strategies to generate competitive advantages. Moreover, to align business management with environmental objectives, it is desirable to integrate digitalization with big data analytics and machine learning within business models (Brenner, 2018).

Digitalization has become a transformative trend reshaping business models and society. It is altering how firms in the digital economy produce and deliver high-quality products and services (Horbach, 2024; Trașcă et al., 2019). The era of the Sustainable Development Goals has also reshaped organizational strategies, emphasizing the need for socially responsible firms that invest in technological infrastructure and motivate others to do the same, thereby fostering a more global and sustainable environment (Di Vaio et al., 2020).

Intangible resources—such as human capital and reporting systems based on environmental, social, and governance performance—constitute critical assets within the resource-based view (RBV). Ecological knowledge within management teams can be shared with stakeholders, promoting sustainable organizational routines (Gómez Martínez et al., 2024; Wicaksari et al., 2024). Digitalization is also recognized as an intangible resource that strengthens competitiveness by optimizing processes through digital tools and facilitating decision-making via business intelligence systems, consistent with the RBV (Pérez Escutia & Fischer de la Vega, 2023). Yiu et al. (2021) further argued that the RBV supports the integration of business intelligence systems into organizational operations through the coordination of certified capabilities and processes. Brenner (2018) complemented this view by emphasizing that digital technologies—such as artificial intelligence and big data—must be aligned with sustainable strategies that foster green innovation and organizational resilience.

Research also highlights managers as key promoters of digitalization in organizations, as they communicate its benefits to employees (Baggia et al., 2019; Isensee et al., 2020). However, the relationship between digitalization and sustainable strategies remains complex. Oláh et al. (2020) examined the relationship between sustainability objectives and digitalization in organizational processes, proposing relevant policies for governments and stakeholders. Gobbo et al. (2018) identified several benefits associated with the incorporation of cloud computing, artificial intelligence platforms, and big data—such as intelligent energy and resource distribution—which contribute to fewer failures in industrial processes, machinery, and supplies. In contrast, some researchers argue that the use of such technologies increases emissions and consumes additional resources and energy (Chiarini et al., 2020). The adoption of clean energy technologies also depends on economic indicators such as taxes, public spending, and trade (Bashir et al., 2023). Despite these divergent perspectives, there is clear academic interest in the relationship between digital technologies and environmental sustainability.

Based on the above arguments, we propose the following hypothesis:

Hypothesis 1 (H1): Sustainable strategies promote the integration of digitalization in manufacturing firms.

To implement sustainable strategies and translate them into improved financial performance, stakeholder involvement is essential—particularly that of investors, governments, employees, suppliers, and customers. Adopting sustainable strategies requires developing specific programs aimed at reducing emissions, improving operational efficiency, and minimizing waste (Eccles et al., 2013). Sustainable firms must assess their sustainability performance by adopting innovative tools and organizational initiatives. According to Engert et al. (2016), the integration of business processes and sustainability provides a path to stability in rapidly changing markets. A strategic focus on sustainability and organizational flexibility also strengthens dynamic business competencies (Meflinda et al., 2018).

Researchers have increasingly adopted the resource-based view (RBV), which emphasizes the efficient and innovative use of energy, resources, and labor to manufacture products and achieve sustainable development in a constantly evolving environment (Cagliano & Behnam, 2019). Recent studies argue that the RBV offers a strategic framework for implementing sustainable strategies that not only reduce environmental impacts but also enhance organizational reputation and create new market opportunities (Barragán-Hernández & Aguilar-Fernández, 2024). Moreover, when these strategies are combined with emerging technologies, they become even more valuable in resource-constrained economies (Cuevas‐Pichardo et al., 2025). Gómez Martínez et al. (2024) reported empirical evidence that stronger sustainability performance is associated with improved corporate reputation, greater stakeholder trust, and enhanced firm performance. Thus, sustainable strategies represent not only an ethical responsibility but also a strategic resource that drives profitability and competitiveness. Organizational learning further reinforces dynamic capabilities from the RBV perspective (Beltramino et al., 2023), and sustainable strategies can therefore be adopted to develop business resources and secure competitive advantages (Barney, 1991).

Existing research supports the view that managers should prioritize sustainable strategies to achieve stronger financial performance (Gorondutse et al., 2020; Ukko et al., 2019). Santis et al. (2016) identified a positive relationship between financial performance and social practices, demonstrating that organizations committed to sustainability improve their financial outcomes over time. Although sustainable management practices are more firmly established in large firms, both small and large organizations continue to make important efforts to enhance performance and incorporate social objectives (Falle et al., 2016).

Based on this evidence, the research proposes the following hypothesis:

Hypothesis 2 (H2): Sustainable strategies positively influence business performance in manufacturing firms.

The literature recognizes that digital technologies that support production processes and product offerings have a direct effect on firm performance (Di Vaio et al., 2022; Restrepo-Morales et al., 2024). By anticipating the benefits of digitalization, firms can transform their operational and strategic processes through Industry 4.0, which involves integrating smart technologies and enhancing traditional management systems (Manresa et al., 2021). Digitalization enables employees to collaborate across departments, improve productivity and work quality, and coordinate more effectively with external business partners across the broader industry ecosystem (Ruivo et al., 2020). Digital information is also central to decision-making, as it allows leaders to visualize strategies and anticipate scenarios (Teerasoponpong & Sopadang, 2022).

Digitalization—particularly through business intelligence systems—provides a tool for analyzing organizational challenges and responding to them in a timely manner (Chen & Lin, 2021). The reasons for implementing information systems vary depending on each organization’s needs, and the perceived benefits differ accordingly.

The incorporation of digital technologies improves profitability by enhancing efficiency, expanding market coverage, and reducing operational costs (Chege & Wang, 2020). Given the economic potential of digitally enabled tools, a central challenge for organizations lies in motivating stakeholders to adopt and use these systems to strengthen analytical capabilities (Kohtamäki et al., 2020). Porter and Heppelmann (2015) argued that, in addition to adopting digital technologies, firms must develop capabilities in research and development, modify routines, and generate new solutions to improve performance.

The positive effect of digitalization on firm performance has been documented across several domains, including product manufacturing, customer service development, competitiveness, and the creation of new business models (Joensuu-Salo et al., 2018). Similarly, Weill and Woerner (2015) reported increases in profit margins and revenue among firms that adopt digital technologies and operate within digital ecosystems. Attewell (1992) demonstrated that firms adopting an absorptive capacity strategy are able to acquire new resources and recognize the value of information generated through digitalization. Innovation becomes the primary mechanism for leveraging knowledge to achieve business success, in alignment with the resource-based view (RBV).

Digitalization also strengthens organizational visibility, understood as the ability to generate, share, and use strategic information. Within the RBV framework, visibility is considered an intangible resource that enhances information-based decision-making (Lopez-Torres et al., 2025). The adoption and implementation of digital technologies drive firm performance by optimizing production processes and improving resource efficiency. Under the RBV, the combination of technological resources creates new dynamic capabilities, particularly in firms located in emerging economies (Cuevas‐Pichardo et al., 2025). Firm performance is not determined solely by the implementation of technology; it also depends on a small or medium-sized enterprise’s ability to absorb and apply external knowledge to digital processes (Cuevas-Vargas & Fernández-Escobedo, 2025). Within this framework, advanced technologies applied to manufacturing firms have been shown to positively influence performance through cost reduction, automation, product innovation, and organizational resilience—even during periods of crisis (Madrid-Guijarro et al., 2025).

Based on the above arguments, we propose the following hypothesis:

Hypothesis 3 (H3): Digitalization fosters business performance in manufacturing firms.

This research therefore proposes that sustainable strategies and digitalization positively influence firm performance. Likewise, sustainable strategies have a positive effect on digitalization (see Figure 1).

The study focuses on small, medium-sized, and large manufacturing firms. To construct the database, we followed the criteria established by the National Statistical Directory of Economic Units (Denue, 2019). This official source provides information on the identification, location, economic activity, and size of more than five million active economic units across Mexico. All information is validated by the National Institute of Statistics and Geography (Inegi, 2021).

Manufacturing firms with 11 or more employees located in the state of Coahuila, Mexico, were selected. Applying these criteria yielded a population of N = 1,270 companies. A pre-test was conducted with directors and managers from different manufacturing firms to ensure that the questionnaire items were clear, understandable, and aligned with the study’s objectives.

A total of n = 120 valid responses were retained for analysis, although more responses were initially received. To ensure data quality and consistency, incomplete questionnaires and responses with inconsistent information were excluded. Participants were individuals responsible for decision-making within their firms (directors and managers).

Data were collected through an online questionnaire distributed directly to each company. The data collection period lasted eight months, beginning in December 2020 and ending in July 2021. The overall sample design was based on the principles of stratified sampling. To accomplish this, stratification criteria were defined according to the study’s objectives, the availability of information, the structure of the population, and feasibility for fieldwork.

The study adopts the structural equation modeling methodology using partial least squares (PLS-SEM), which requires defining and formalizing the nature of the research constructs. The model is composed of reflective composites (Jarvis et al., 2003). The unobserved variables analyzed are sustainable strategies, digitalization, and firm performance. Each construct is composed of the manifest indicators required to specify and measure the model (Willems, 2000). These composites were developed based on an extensive review of the literature, with an emphasis on empirical studies in various application contexts that provide sufficient support for their measurement (see Table 1).

Independent variables produce indirect or direct effects on other variables (Nitzl et al., 2016). Sustainable strategies serve as an independent variable that captures the extent to which a firm incorporates global sustainability indicators into its operations and departments. This variable was measured using five indicators (see Table 1) on a Likert scale ranging from 1 to 5, where 1 represents minimal integration and 5 represents maximum integration. Adopting sustainable strategies reflects an innovation process that improves the business model and internal processes, thereby influencing financial performance (Grayson et al., 2018).

Dependent variables are a central component of the study (Nitzl et al., 2016). Firm performance is one of the primary dependent variables (see Table 1). It consists of four indicators that capture an organization’s performance over the past two years, focusing on profitability, market share, competitive price management, and value chain integration within a unified system (Elbashir et al., 2008; Gunday et al., 2011). Firm performance was measured on a Likert scale ranging from 1 to 5, where 1 represents very unfavorable performance and 5 represents very favorable performance. These indicators are based on prior empirical studies and measurement standards widely accepted in organizational research (Powell, 1995).

Digitalization is also treated as a dependent variable and is composed of seven indicators that capture the integration of various technological platforms and tools. The key objective of digitalization is to enhance internal process capabilities and generate competitive advantages in the marketplace. Digitalization was measured on a Likert scale ranging from 1 to 5, where 1 represents minimal integration and 5 represents maximum integration. The measurement of this variable is supported by previous field studies examining the application of technological tools to improve business processes, which in turn positively affect the supply chain and overall firm performance (Hennelly et al., 2020).

This research examines firm size, manager age, and manager experience as control variables to better understand their influence on the use of sustainable strategies and digitalization in manufacturing firms. These variables have been widely used in previous studies (Cenamor et al., 2019; Chen & Lin, 2021; Heredia-Calzado & Duréndez, 2019; Peters et al., 2016).

Firm size, measured by the number of employees, may indicate business stability and sustained growth (Kohtamäki et al., 2020) and may also serve as a proxy for technological infrastructure (Abou-Foul et al., 2021). Prior studies have identified a negative impact of older managers on organizational performance, largely attributed to resistance toward adopting new digital technologies (Kunze et al., 2013). In contrast, years of managerial experience in manufacturing firms are generally considered a positive factor for decision-making.

Dummy variables were created to measure two of the control variables. For manager age, individuals aged 25 to 49 were coded as 0, and those aged 50 or older were coded as 1. For managerial experience, those with 1 to 15 years of experience were coded as 0, while those with more than 15 years of experience were coded as 1. Firm size was measured using the total number of employees.

The research model applies multivariate analysis to examine the relationships among composites formed by multiple observable indicators, allowing the study hypotheses to be tested (Monecke & Leisch, 2012). Marcoulides (1998) demonstrated that structural equation modeling is widely used in the social sciences because many conceptual variables cannot be directly observed. The conceptual model (see Figure 1) was assessed using the SmartPLS software (SmartPLS, 2020) and the partial least squares algorithm.

Model estimation begins by running the PLS algorithm and refining the external model to remove indicators with standardized loadings lower than 0.707 (Carmines & Zeller, 1979). The communality of an indicator (λ²) represents the proportion of an item’s variance explained by the composite; values of λ² ≥ 0.707 are considered acceptable. Indicators with loadings above 0.40 may be retained if the overall reliability, validity, and quality thresholds of the model are satisfied and if their removal does not improve the measurement model. For this reason, traditional measures of internal consistency, reliability, and validity remain appropriate for composite-based analysis (Henseler, Ringle, et al., 2016).

The quality criteria for the measurement models are presented in Table 2. Composite reliability values exceed the required threshold, supporting the internal consistency of each construct. Composite reliability is preferred over Cronbach’s alpha in PLS modeling (Marcoulides, 1998) and is generally considered a more consistent estimator (Dijkstra & Henseler, 2015).

Cronbach’s alpha ranges from 0 to 1. Darren and Mallery (1995) suggested that values below 0.50 are unacceptable, values between 0.80 and 0.90 are considered good, and values above 0.90 indicate excellent reliability. Table 2 reports the Cronbach’s alpha values along with their corresponding assessments.

The average variance extracted (AVE), proposed by Fornell and Larcker (1981), is a more conservative measure than composite reliability. An AVE value greater than 0.50 indicates that each composite explains at least 50% of the variance of its manifest indicators. As shown in Table 2, the latent variables achieve convergent validity because their AVE values exceed the recommended threshold.

Table 3 presents the Fornell and Larcker (1981) criterion used to assess discriminant validity. According to this criterion, the correlations among latent variables must be lower than the square root of the average variance extracted (AVE) for each construct. In this study, all constructs satisfy this requirement, indicating that discriminant validity has been achieved.

Henseler, Hubona, et al. (2016) conducted simulation research demonstrating that discriminant validity is most effectively assessed using the heterotrait–monotrait ratio (HTMT). This ratio is calculated by dividing heterotrait correlations by monotrait correlations. When all HTMT values fall below 0.90—or below 0.85 under more conservative criteria—the constructs are considered to have achieved discriminant validity. As shown in Table 4, all constructs in the proposed conceptual model meet this requirement.

In this section, the proposed structural model is evaluated using the bootstrapping procedure to determine its reliability. To ensure robust results, 10,000 subsamples were generated (Hair et al., 2013, 2014, 2017; Marcoulides, 1998; Monecke & Leisch, 2012).

The standardized root mean square residual (SRMR), the unweighted least squares discrepancy (d_ULS), and the geodesic discrepancy (d_G) all fall within acceptable ranges. For each index, the original value is compared with the confidence interval derived from the bootstrapping distribution. The confidence interval must include the original value for the model to be considered a good global fit. Confidence intervals based on the 95% or 99% levels are typically used to evaluate model adequacy (Henseler, 2017; Henseler, Hubona, et al., 2016). Both the unweighted least squares discrepancy and the geodesic discrepancy are expected to fall within their respective bootstrapped confidence intervals (Dijkstra & Henseler, 2015). Table 5 presents the global model fit results.

The dominant model-fit criterion is the standardized root mean square residual (SRMR). This statistic reflects the square root of the sum of squared differences between the observed correlations and the model-implied correlations. According to Hu and Bentler (1998), an SRMR value below 0.08 indicates an adequate model fit, and Williams et al. (2009) noted that values below 0.10 can also be considered acceptable. The obtained value of SRMR = 0.063 is therefore well below the recommended threshold (see Table 5). Overall, the global model fit indices suggest a satisfactory model fit. Both the unweighted least squares discrepancy (d_ULS) and the geodesic discrepancy (d_G) meet the required criteria and fall within the corresponding bootstrapped confidence intervals.

The structural model becomes particularly relevant when examining the predictive capacity of the constructs and their interrelationships. The evaluation of the path coefficients (β), together with the F² and R² values, provides insight into the strength and explanatory power of the model (see Figure 2).

The path coefficients serve as construct-specific measures used to evaluate the study’s hypotheses. Table 7 reports these results.

Hypothesis 1 (H1) is supported: sustainable strategies positively influence digitalization, with β = 0.717, p = .000, and a confidence level above 99% (Baumgartner & Rauter, 2017; Ukko et al., 2019). This finding aligns with Isensee et al. (2020), who argued that environmentally oriented strategies shape the extent of digitalization by aligning the firm’s mission, vision, routines, and actions with sustainability and technological development.

Hypothesis 2 (H2) is also supported: sustainable strategies have a positive effect on firm performance, with β = 0.368, p = .005, and a confidence level above 99% (Gorondutse et al., 2020; Santis et al., 2016). This finding is consistent with Meflinda et al. (2018), who noted that organizational success depends on balancing environmental, social, and economic goals while cultivating knowledge creation and dynamic capabilities.

Hypothesis 3 (H3) is confirmed as well: digitalization positively influences firm performance, with β = 0.225, p = .049, and a confidence level above 95% (Abou-Foul et al., 2021). As Martín-Peña et al. (2019) suggested, manufacturing firms benefit from integrating digital technologies into their business models by responding more effectively to evolving customer expectations.

The mediation analysis was conducted by examining both the statistical significance and the magnitude of the indirect effects (Hayes, 2009). The results indicate a significant indirect effect linking the predictor variables—sustainable strategies and digitalization—to firm performance. The bootstrapping procedure shows that digitalization partially mediates the relationship between sustainable strategies and firm performance, with an indirect effect of β = 0.162 and a total effect of β = 0.691, supported by p = .048 (see Table 6).

Regarding the control variables (see Figure 2 and Table 7), manager age shows a negative and significant influence on firm performance, with a coefficient of β = –0.204. This suggests that younger managers—who tend to be more open to technological change—are more likely to promote the use of digitalization and sustainable strategies within their organizations. Top management guidance is an essential factor in cultivating an information-technology-oriented culture (Stewart & Gapp, 2014). In contrast, managerial experience (measured in years leading the firm) and firm size (measured by number of employees) exhibit negative but non-significant relationships with firm performance, with coefficients of β = –0.045 and β = –0.062, respectively.

An alternative model was estimated to assess the robustness of the structural model. The robustness model, which specifies the composites as formative, is presented in Figure 3 (Cenfetelli & Bassellier, 2009). The results are consistent with those obtained from the original reflective model (see Figure 2), showing a significant explanatory effect (R² = 0.524 and R² = 0.432). However, in the alternative model, the mediating effect was not statistically significant.

The direct relationships remain stable. The relationship between sustainable strategies and digitalization is supported (β = 0.724), indicating that sustainable strategies positively influence digitalization. The relationship between sustainable strategies and firm performance is also supported (β = 0.470), demonstrating a substantial effect on firm performance. Finally, the relationship between digitalization and firm performance is confirmed (β = 0.161), indicating that digitalization positively influences firm performance.

Consequently, the robustness model shows results that are consistent with the original analysis, with the exception of the mediating relationship between sustainable strategies, digitalization, and firm performance, which was not significant. Overall, the findings confirm that both digitalization and sustainable strategies are positively related to firm performance.

This research offers important implications for managers of manufacturing firms and for policymakers. For managers, the findings demonstrate the value of adopting innovative strategies that promote the digital transformation of business management. Management strategies should emphasize digital competitiveness by cultivating a strategic digital vision, aligning artificial intelligence and information generation with competitive advantage, and adopting a proactive digital orientation that supports differentiation and growth (Lozano et al., 2024; Pérez Escutia & Fischer de la Vega, 2023; Porras Sandoval et al., 2025). This approach is reinforced by integrating sustainable strategies as corporate pillars that promote a sustainable organizational culture, increase energy efficiency, and reduce waste (Cuevas-Vargas & Fernández-Escobedo, 2025; Cuevas‐Pichardo et al., 2025; Ponce et al., 2024).

For policymakers, the findings underscore the need to promote measures and incentives that enable manufacturing firms to adopt and develop digital technologies in their management processes, while also facilitating investment in energy-saving infrastructure projects, climate policies, and tax incentives (Martinez & Terrazas-Santamaria, 2024). It is essential to implement regulatory frameworks that encourage investment in green technologies, advance sustainability through industrial development programs, and promote certifications based on sustainable technologies (Horbach, 2024; Jamil et al., 2025; Restrepo-Morales et al., 2024). By adopting sustainable strategies and digitalizing their operations, firms can strengthen their competitiveness and contribute to sustainable economic growth and long-term job creation.

This study has several limitations that offer opportunities for future research. The analysis examined the impact of digitalization and sustainable strategies on the financial performance of firms, and the results indicate that both variables are significant predictors of performance. However, conclusions may differ if samples are drawn from other states in Mexico. The study proposes a pathway for manufacturing firms to transition toward customer-centered business models by integrating technology, data analysis, and sustainable strategies to generate social and environmental value.

Future research should extend the analysis to additional Mexican states and, potentially, to other Central American countries with differing economic, cultural, and political contexts. Because this study focused solely on the manufacturing sector, future research should also examine other sectors that may have characteristics distinct from those analyzed here. Finally, the research is cross-sectional, relying on primary data collected at a single point in time. Longitudinal research would provide stronger evidence by determining whether the observed relationships remain consistent over time.