1. Introduction

The literature establishes that the last decade has been characterized by an increasing number of natural disasters caused by global warming, and it is expected that these types of phenomena will continue to increase in the following years (IPCC, 2023; WMO, 2024). From this perspective, drastically reducing the level of pollutant emissions into the atmosphere requires transformative changes across all sectors of the global economy (IPCC, 2022). Specifically, several international agencies, government agencies, and scientific community suggest that Industry 4.0 technologies (I4.0T), and circular economy practices (CE) represent a crucial strategy to significantly improve sustainable development (SD) by reducing greenhouse gas emissions, which will allow achieving net-zero emissions goals (European Commission, 2020; IEA, 2022; Saccani et al., 2023; Sassanelli et al., 2023; Acerbi et al., 2024; Ciano et al., 2025).

In the light of this scenario, the literature calls for manufacturing firms to adopt more digital, and efficient production processes (Gatell & Avella, 2024), through the simultaneous implementation of I4.0T, CE practices and SD strategies (Cannas et al., 2025), particularly if we consider that I4.0T can be defines as "a combination of different technologies such as additive manufacturing, simulation, robots and autonomous vehicles, augmented and virtual reality, IoT, cloud and cybersecurity" (RüBmann et al., 2015, p. 56); CE can be defined as "an economic system based on the reuse, reduction, recycling and extraction of end-of-life materials to achieve long-term sustainable development goals" (Kirchherr et al., 2017, p. 222), and SD can be defined as "those activities that are oriented towards the production of eco-friendly products with the environment, in compliance with environmental protection and for the benefit of society, the economy and the environment (Singh et al., 2022, p. 3)".

However, even though theoretical and empirical evidence of the effects of I4.0T on CE and SD practices has been provided, there are still many doubts in the literature and among company managers about the results obtained (Varriale et al., 2024). Particularly because while some studies have found a positive relationship between I4.0T and CE practices (e.g. Rosa et al., 2020; Ciliberto et al., 2021), others have found a negative relationship (e.g. Ghobakhloo & Fathi, 2020; Dieste et al., 2023), while, theoretically, other studies are not clear how I4.0T and CE can potentiate research and other (e.g. Sehnem et al., 2025; Ciano et al., 2025). In this context, the link between I4.0T, CE and SD practices are open to debate in the literature (Cannas et al., 2025), especially in the manufacturing industry of developing countries (Ciliberto et al., 2021; Percin et al., 2025).

Although recent studies have provided robust theoretical and empirical evidence of the link between I4.0T and CE and SD practices in developing countries (e.g. Kayikci et al., 2022; Dwivedi et al., 2022; Kumar et al., 2024), especially in the manufacturing industry in Mexico (e.g. Mora-Contras et al., 2023; Rodríguez-González et al., 2023; Maldonado-Guzmán & Garza-Reyes, 2023), which is one of the developing countries that have a total cost of depletion of environmental resources equivalent to 4.6% of GDP, with the manufacturing industry generating the greatest negative impact on the environment representing 15% of total GDP, and the automotive and aerospace industries contributing just over 5% of that total (INEGI, 2022), the full integration and potential of the relationship between these three concepts is still in the early stages of adoption and cannot be considered conclusive (Percin et al., 2025).

Additionally, the digital transformation of companies is essential, especially after having adapted their work systems to the introduction of I4.0 digital technologies during the COVID-19 pandemic to become more competitive and survive (Lafuente et al., 2023; Escribá-Carda et al., 2024). Therefore, the adoption of I4.0 digital technologies such as big data, algorithms, cloud computing, and social networks not only helps companies improve their competitive advantages (Rojas-Segura et al., 2023) but is also an essential condition for their market participation (Lafunte et al., 2022). In this context, digital transformation contributes to the development of skills for adding value through customized solutions to specific problems in response to specific company challenges (Lafuente & Sallan, 2024), as well as greater cost efficiency for profitable customization of supplies (Vaillant et al., 2025).

In this context, digital transformation not only helps manufacturing companies align their processes to current business scenarios to obtain financial and economic benefits (Vaillant & Lafuente, 2024), but also to obtain non-financial benefits such as, for example, the benefits of customer orientation (Sjödin et al., 2019), operational and competitive performance (Vaillant & Lafuente, 2024), ecological benefits (Lafuente & Vaillant, 2023a) and competitive efficiency (Lafuente & Vaillant, 2023b). Therefore, digitalization provides I4.0 technologies that can be used to enhance value creation and appropriation by manufacturing firms (Wamba et al., 2017; Kohtamäki et al., 2019), providing them with both financial and economic benefits (Abou-Foul et al., 2021; Gebauer et al., 2021; Yang et al., 2023) and non-financial benefits (Lexutt, 2020).

Understanding digitalization processes in emerging contexts becomes essential for organizations to align their operations, business models, and strategic capabilities with corporate objectives of growth, efficiency, and competitiveness. This is noted by Ács et al (2022) in their article, where they highlight that the development of a digital ecosystem-measured through the Digital Ecosystem Index (DEI) in 16 Latin American and North American countries-allows companies to better connect with platforms, users, and governments, generating synergies that drive value creation in dynamic environments. In emerging markets, where technological, institutional, and infrastructure barriers are often more pronounced, this digitalization capacity not only improves internal processes but also enables new channels for development, geographic expansion, and adaptation to change. Consequently, integrating these digital processes into corporate strategy fosters improved organizational performance-greater agility, better data-driven decisions, and greater market reach-and, consequently, the achievement of corporate objectives in an increasingly digital and competitive environment.

The objective of this study is to analysis the connection between I4.0T and CE on SD of manufacturing firms in the automotive and aerospace industries in Mexico, and response the following research question: What is the relationship between Industry 4.0 digital technologies in CE and SD practices in manufacturing firms? a survey was used using a sample of 378 companies and the statistical technique of Partial Least Squares Structural Equation Modelling (PLS-SEM), with the use of SmartPLS 4.0 software (Ringle et al., 2024). Furthermore, from a theoretical point of view, this study adds significant value to the literature by offering an empirical analysis of the digital ecosystem in emerging economies, an area that has been little explored compared to the contexts developed by authors such as Agrawal et al (2022) and Ciano et al (2025), therefore, to complement and expand the limited body of knowledge, this paper addresses this gap in the literature.

2. Literature Review

3. Methodology

4. Results

The use of PLS - SEM statistical technique to respond to the hypotheses established in the research model is fundamentally due to two basic aspects. (1) It is the most appropriate statistical technique for the analysis of theories, which have not been widely developed in the literature in the various disciplines of knowledge (Hair et al., 2019). (2) It is the most appropriate statistical technique when the objective of the research is the prediction and explanation of the different concepts used in the research model (Hair et al., 2019). Therefore, the PLS-SEM statistical technique facilitates the explanation of the measurement error of the three concepts used in this study, as well as the multiple regression of the range of scores of the link between I4.0, SD, and CE in manufacturing firms (Hair et al., 2019).

4.2. Structural Model

The structural model was evaluated through the most recommended indicators in the PLS-SEM literature: coefficient of determination (Adjusted R²), multicollinearity test (VIF), and p-value (Hair & Sarstedt, 2021). To obtain the values of these indicators, the bootstrapping procedure was carried out with 5,000 subsamples using SmartPLS 4.0 software (Ringle et al., 2024), and the results indicate the existence of an acceptable statistical level, obtaining Adjusted R² values (SD: 0.251; CE: 0.063) higher than the value recommended in the literature (Henseler et al., 2014; Hair & Sarstedt, 2021). Multicollinearity was assessed using VIF and the results obtained indicate that the minimum and maximum VIF values are 1.136 and 4.652, which confirms the non-existence of multicollinearity problems (Pallant, 2020).

The significance of the research model was assessed using the p-value, which are considered significantly acceptable if the p-value is less than 0.05 (Hair & Sarstedt, 2021). The results obtained indicate that the p-values (I4.0T-SD: 0.001; I4.0T-CE: 0.000; CE-SD: 0.000; I4.0T-CE-SD: 0.000) are less than 0.05, which indicates the acceptance of the hypotheses raised in the research model. Figure 1 shows these results in greater detail.

Figure 1 shows that the estimated data verify our argument that I4.0T has a significant positive effect on SD (0.200; p-value 0.001), which provides evidence in favor of hypothesis H1, these results being consistent with those found in recent studies by Rifqi et al. (2024) and Percin et al. (2025), which demonstrates the theoretical evidence found in the literature that I4.0 digital technologies help manufacturing companies improve SD practices. Figure 1 also verifies our argument that I40T has a positive effect on CE (0.257; p-value 0.000), these results showing evidence in favor of hypotheses H2 and are in line with both recent studies by Dantas et al (2021) and Agrawal et al (2022), as well as the theoretical evidence found in the literature that establishes a close connection between both constructs.

The Figure 1 results also show that CE practices have a positive effect on SD of manufacturing firms (0.415; p-value 0.000), which provides evidence in favor of hypothesis H3, These results are similar to those found in recent studies by Dieste et al (2023) and Sonar et al (2024a), which demonstrates the theoretical evidence found in the literature that establishes that CE practices are essential for improving the SD of manufacturing companies. Finally, Figure 1 also verifies our argument that CE practices can act as a variable mediator in the relationship between I4.0T and SD (I4.0^CE^SD 0.199; confidence intervals: 2.5% = 0.050; 97.5% = 0.154), thereby providing evidence in favor of hypothesis H4 and are in line with both recent studies by Singh et al (2024) and Samadhiya et al (2023), and with the theoretical evidence found in the recent literature establishing that the level of SD increases when CE practices act as a mediating variable between I4.0T and SD.

5. Discussion

The results obtained in this study support our argument of the existing link between I4.0T in SD activities, these results being in line with those found by Obaideen et al (2022), Bag and Christiaan (2022) and Vu et al (2023). The main reason that could explain these results is that the adoption of I4.0T promotes sustainable production practices and resource efficiency, which can help companies improve their level of sustainability, reduce waste and pressure on the use of natural resources. These results confirm the findings of Lezoche et al (2020), who demonstrated that by integrating I40T such as wireless networks, cloud computing and the Internet of Things, agro-industrial companies can obtain additional data (e.g. water, soil, humidity, temperature and radiation) that can help them reduce negative impacts on the environment and SD. Therefore, I4.0T improve SD activities by increasing productivity, reducing industrial waste and ecological deterioration.

The results obtained also support our argument that I4.0T has a positive effect on CE practices, these results being like to those obtained by Rosa et al. (2020), Awan et al (2021) and Agrawal et al (2022). The essential reason that could explain this result is that the adoption of I4.0T, such technologies as big data, blockchain, the Internet of Things, artificial intelligence, and machine learning enhance business circularity. These results confirm the findings of studies by Sutar et al (2024) and Sharma et al (2024), who found that these types of I4.0T digital technologies significantly improve the traceability and transparency of material reuse, recycling, and remanufacturing, while reducing raw material use and industrial waste. Therefore, the results obtained in this study demonstrate the need for company managers to integrate I4.0T with CE practices and sustainability strategies to achieve SD objectives.

The results obtained also support our argument of the positive relationship that CE practices have on SD activities, these results being in line with those obtained by Voukkali et al (2023), Axhami et al (2023) and Strippoli et al (2024). The essential reason that could explain this result is that CE practices. They improve resource efficiency and productivity in circular production processes. These results confirm the findings of Islam and Zheng (2024), who demonstrated that CE practices are essential for improving SD activities, reducing industrial waste, using renewable energy, and adopting more sustainable consumption. Therefore, the results of this study provide robust empirical evidence demonstrating that the implementation of CE practices helps companies utilize renewable resources such as water and energy, as well as protecting the environment and terrestrial and marine ecosystems.

Finally, the results obtained also support our argument that CE practices play a mediating role between I4.0T and SD, these results being like those found by Lim et al (2021), Boeing et al (2022) and Samadhiya et al (2023). The main reason that could explain these results is that the integration of CE practices not only favors the improvement of SD through the adoption of I4.0T in production processes but also allows manufacturing companies to significantly reduce production costs. These results confirm the findings of the study conducted by Percin et al (2025), who found that the integration of I4.0 digital technologies such as sensors, RFID, remote sensing, big data, Internet of Things, and artificial intelligence contribute to real-time traceability and operational efficiency. By implementing CE practices at the same time. Therefore, I4.0T improves SD activities, when CE practices are applied simultaneously, which increases productivity and reduces industrial waste in manufacturing companies.

6. Conclusions, Limitations and Future Research Directions

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