1. Introduction
In the current context of uncertainties and rapid changes at the global level, a resilient food system is essential for ensuring food security and promoting sustainable development [
1]. Governments must take proactive action to identify and address risks within the food system [
2]. Building a resilient food system involves not only identifying and eliminating weaknesses but also creating mechanisms and structures that allow for rapid adaptation and recovery when facing uncertainty and changes [
3,
4]. Therefore, the concept of resilience provides a valuable approach to addressing the complexity and fragility of the food system and promoting a more robust and sustainable approach to food security [
5,
6,
7].
Climate change, demographics, and pandemics have intensified pressure on global food systems. Recent data underscore the critical imperative to double food production by 2050 to meet the escalating demands of the worldwide population [
8]. Concurrently, approximately one-third of all food produced globally, amounting to 930 million tons, is lost or wasted throughout the agri-food supply chain, resulting in approximately 800 million people experiencing hunger [
9]. These challenges underscore the urgent need to address issues pertaining to food security, reduce food loss and waste, and sustainably manage natural resources within the agri-food sector. Furthermore, it is essential to recognize the significant environmental impacts associated with food production and consumption. Highlighting the importance of the twin transition—both social and digital—towards sustainable development can mitigate these environmental impacts [
8]. These challenges entail the development of resilient and sustainable food systems that can cope with increased food demand and reduce environmental impact. This development involves innovations in food processing, adapting to climate change, and promoting more sustainable agricultural practices [
10,
11].
The use of digital technologies and innovation in the agri-food sector represents a crucial opportunity for addressing significant challenges related to food security, environmental sustainability, and reducing inequality [
12,
13]. Integrating digital solutions and adopting more efficient agricultural and food practices contribute to achieving global sustainable development goals and improving the quality of health and life [
14]. This digitization can provide innovative solutions to challenges faced by the agri-food sector, such as climate change, natural resource management, and increasing food demand. Digitization in the agri-food sector presents innovative technology such as artificial intelligence, remote sensors, agriculture precision, and intelligent farming while also integrating social media for better communication and blockchain technology for reliable data collection and enhanced transparency.
Artificial intelligence enables farmers to analyze vast amounts of data to make data-driven decisions in real time [
1]. Remote sensors provide continuous monitoring of crops, soil moisture levels, and environmental conditions, allowing for timely interventions and precise resource allocation. Agriculture precision technologies, such as GPS-guided machinery and variable rate application systems, enable farmers to optimize inputs, minimizing waste and maximizing productivity [
6]. Smart farming integrates these technologies into comprehensive management systems, fostering sustainable practices and enhancing the resilience of agricultural production [
8]. Social media platforms play a vital role in fostering communication and knowledge exchange among farmers, producers, distributors, and consumers, facilitating discussions on sustainable agriculture [
15]. Social media also serves as a powerful marketing tool, enabling more comprehensive outreach and building consumer trust. Likewise, the adoption of blockchain technology ensures reliable data collection, enhances transparency, and strengthens security measures in the agri-food supply chain, bolstering consumer confidence and addressing concerns regarding data protection [
16].
Adopting digital technologies creates more resilient, sustainable, and future-oriented food systems, thereby contributing to the achieving of the UN’s Sustainable Development Goals (SDGs) [
17]. These technologies can improve decision-making processes and contribute to the development and implementation of more effective policies in all areas of SDG action. The use of these technologies can also pose challenges, such as data protection and information security, as well as equitable access to digital technologies, which need to be addressed to maximize the benefits of these tools in promoting sustainable development [
13,
14].
This paper aims to investigate the impact of digital transformation on food security and advancing SDGs in the European Union, focusing on the relationship between the Digital Economy and Society Index (DESI) and food-related SDGs. It addresses a gap in the academic literature by providing a detailed analysis of how different components of the digital economy, such as connectivity, digital skills, internet usage, and digital public services, influence the achievement of food security and SDGs. The paper stands out for its innovative approach in providing fresh perspectives on the critical role of digital technologies in poverty alleviation, health enhancement, and inequality reduction. These insights contribute to a deeper understanding of the multidimensional relationship between digital transformation and food security, laying the groundwork for more informed policymaking and strategic interventions.
The study posits two fundamental research questions that form the basis of its two hypotheses. First, it explores whether digital transformation significantly contributes to the achievement of food-related SDGs across European Union countries. Secondly, it investigates the possibility of categorizing European countries into distinct clusters based on their levels of digital economy development and their progress towards SDGs related to food security. These research questions guide the examination of the complex relationship between digital transformation, SDGs, and food security within the European context. Both research questions align with the European Union’s active efforts to address challenges within the agri-food system and its focus on research and innovation policy to promote sustainable, healthy, climate-resilient, and inclusive food systems [
18].
This study’s importance lies in its comprehensive exploration of the pivotal role of digital transformation in shaping food security outcomes, its novel insights into leveraging digital technologies for achieving SDGs, and its contribution to advancing understanding in the field of sustainable development and digital transformation at the EU level.
4. Results
Hypothesis H1 used structural equation modeling in the partial least squares variant. The software used is SmartPLS v3.0 [
89].
Figure 1 presents the empirical model created to quantify the relationships between the components of DESI, SDGs related to food, and the overall SDG index. The relationship between these components is formative, indicating that they collectively contribute to the overall assessment of SDGs related to food.
An essential indicator of the validity and reliability of a formative SEM model is the variance inflation factor (VIF). VIF is a measure used to assess the degree of collinearity among explanatory variables. Collinearity is a problem when two or more independent variables in a regression model are strongly correlated with each other. This correlation can lead to misunderstandings in interpreting path coefficients and their precision [
86].
Table 2 presents the VIF values for the SEM model.
Table 2 reveals that the VIF values are below 3, indicating good model validity. SRMR (standardized root mean square residual) and NFI (normed fit index) are measures used in SEM model analysis to assess how well the model fits the observed data and to make adjustments and improvements based on the specific research needs and available data [
87]. In the proposed SEM model, SRMR has a value below 0.08 (0.042), and NFI has a value above 0.9 (0.906). The model is adequate and fits well with the observed data.
A bootstrap procedure with bias-corrected, bidirectional, and significance level set at 0.05 enables the calculation of path coefficients in order to test Hypothesis H1. Bootstrap is a technique of repeated sampling of observations from the dataset with replacement, allowing for estimation of the sampling distribution of a statistic [
86]. This method generates robust estimates of path coefficients and their associated standard errors, which are essential for assessing the significance of relationships between variables.
Table 3 presents the path coefficients of the SEM model.
The use of a bidirectional approach allows for the examination of both positive and negative effects, capturing the entire spectrum of possible relationships between digital transformation and SDGs related to food.
Table 3 presents the path coefficients for the relationships between DESI, the SDG Index, and the food-related SDGs. The path coefficient for the relationship between DESI and the SDG Index is 0.016. This coefficient indicates a weak association between the Digital Economy and Society Index and the SDG Index, and the high
p-value (0.889) suggests that this association is not statistically significant.
In contrast, the path coefficient for the relationship between DESI and the food-related SDGs is 0.417. This value indicates a significant and positive association between the Digital Economy and Society Index and the food-related SDGs. Furthermore, the p-value < 0.001 suggests that this association is statistically significant. The results show that digitalization, illustrated by DESI, has a significant and positive impact on achieving food-related SDGs, although the association with the overall SDG Index is not statistically significant.
The model was modified to determine the individual influences of the digital economy on SDGs related to food, defining latent variables such as GOAL 1: No Poverty, GOAL 2: Zero Hunger, GOAL 3: Good Health and Well-being, GOAL 10: Reduced Inequality, alongside the SDG Index and Digital Economy and Society Index. The SEM model remains formative (
Figure 2).
Also, in this model, VIF has values below 3, indicating the model’s good validity (
Table 4).
In the modified SEM model, SRMR has a value below 0.08 (0.04), and NFI has a value above 0.9 (0.913). The model is adequate and fits the observed data well.
The bootstrap procedure, being bias-corrected, bidirectional, and having a significance level of 0.05, generated the path coefficients of the modified SEM model.
Table 5 presents the path coefficients of the modified SEM model.
The analysis of path coefficients shows mixed results regarding the significance of the individual influences of the digital economy on achieving SDGs related to food. For SDG 10, the path coefficient is 0.341, with a standard deviation of 0.108. The T-value is 3.165, indicating statistical significance at a confidence level of 0.002, underscoring a significant relationship between the Digital Economy and Society Index and the goal of reducing inequality. In the case of SDG 1, the path coefficient is 0.229, with a standard deviation of 0.11. The T-value is 2.085, with a p-value of 0.038, indicating a significant relationship between the DESI and the goal of poverty eradication, but at a lower level of significance. Investigating the relationship between DESI and SDG 2 reveals that the path coefficient is 0.019, with a standard deviation of 0.14. The T-value is 0.134, and the p-value is 0.893, indicating no significant relationship between the DESI and the goal of hunger alleviation. The influence of DESI on SDG 3 shows that the path coefficient is 0.257, with a standard deviation of 0.102. The T-value is 2.524, with a p-value of 0.012, indicating a significant relationship between DESI and the goal of promoting health and well-being. The influence of DESI on the SDG Index generates a path coefficient of 0.036, with a standard deviation of 0.104. The T-value is 0.347, and the p-value is 0.729, indicating that, as in the case of the initial model, there is no significant relationship between the DESI and the overall SDG Index. These results suggest that digital transformation can have a significant impact on specific SDGs, such as poverty alleviation, reducing inequality, and improving health and well-being. However, it may not significantly influence hunger alleviation.
The results of the investigations conducted using SEM models partially validate Hypothesis H1. DESI influences the reach of the food-related SDGs overall, but individual influences are significant in the cases of SDG 1, SDG 3, and SDG 10. In the case of SDG 2, no significant influences were found. These findings suggest that the adoption and development of digital technologies could play an essential role in poverty reduction and improving the health and well-being of the population. However, concerning hunger alleviation, the impact of digital technologies may be more limited or influenced by other variables and specific contexts. It is essential to continue exploring and investigating the role and potential of digital technologies in achieving SDGs and to identify effective ways to integrate them into the food system to promote a more equitable, sustainable, and resilient approach.
Hypothesis H2 involves cluster analysis conducted within EU countries based on the digital economy illustrated by DESI and sustainability in the food domain illustrated by SDGs related to food. Ward’s method, which used squared Euclidean distance intervals, was an adequate choice for cluster analysis.
Figure 3 illustrates the dendrogram depicting the three clusters obtained. Ward’s method is known for its ability to form compact and homogeneous groups that are easy to interpret [
90]. This method minimizes the variation within each cluster and maximizes the variation between clusters, leading to the formation of well-defined and distinctive groups. Using squared Euclidean distance is suitable for continuous data and can efficiently measure differences between observations in multidimensional space.
Table 6 presents the three distinct clusters, labeled A, B, and C, built using Ward’s method, which include countries grouped homogeneously based on indicators of the digital economy and sustainability in the food area. When comparing these clusters with the EU mean, we can observe the differences and similarities among the member countries of the European Union regarding the level of digitization and progress in achieving SDGs.
All countries in the three clusters stand out with high values for SDG1, close to the EU average, illustrating a good situation for EU countries in combating poverty.
Cluster A comprises countries characterized by relatively high values of connectivity, digital public services, human capital, and digital technology integration indicators. These countries, such as France, Germany, and Spain, stand out for their efforts in combating poverty (SDG1) and ensuring good health and promoting well-being (SDG3). However, most of these countries are below the European mean in reducing inequality (SDG10), and half of the countries in cluster A fall below the EU average in achieving zero hunger (SDG2).
In Cluster B, values are more moderate but still significant, suggesting a medium level of digital technology development and progress in achieving SDGs. These countries, such as Belgium, Austria, and Finland, demonstrate a solid commitment to achieving food-related SDGs as well as advancing their digital capabilities. The cluster’s average for all SDGs related to food is around or above the EU average. On the other hand, Cluster C includes countries with lower values of digitalization indicators and modest achievements in SDGs. These countries, such as Romania and Bulgaria, face significant challenges, particularly in promoting health and well-being and countering inequality.
The results of the cluster analysis indicate that European countries can be grouped into homogeneous clusters regarding the digital economy and food-related SDGs, thus validating Hypothesis H2.
5. Discussion
A variety of factors influence the food system, and its resilience depends on its ability to cope with these influences and respond efficiently to disruptions [
91,
92]. Identifying and understanding these factors is essential for developing and implementing strategies to support a more resilient and adaptable food system to changes and crises that may arise [
93,
94]. Evaluating and addressing these factors allows academics to contribute to building a safer and more sustainable food system for the future [
6,
95,
96].
Agriculture and the food industry play an essential role in the global and regional economy, driving development and employment in many countries [
97,
98]. However, these sectors face significant challenges in adapting to new technologies and trends, as well as understanding how digital transformation can improve the efficiency and sustainability of the entire food chain [
99,
100].
The integration of digital technologies in the food domain not only improves the efficiency and quality of production and distribution processes [
59] but also contributes to increasing the resilience of the food system by facilitating adaptation to changes in the business environment and market requirements [
12]. The use of digital technology can radically transform the way food operations are managed, allowing for greater flexibility and efficiency in the face of continuously changing challenges and opportunities [
101].
According to this research results, digitalization illustrated by DESI has an impact on achieving food-related SDGs, but this impact is more significant in some areas than others, partially validating Hypothesis H1. DESI significantly and positively influences goals aimed at poverty eradication, health promotion, and inequality reduction (SDG1, SDG3, and SDG10). However, for SDG 2, which aims to combat hunger, no significant influences of DESI were observed. This finding highlights that the impact of the digital economy on various issues of sustainable development in the food domain can vary and sometimes even be contradictory, as shown by Wang et al. [
6], Bachmann et al. [
17], Baierle et al. [
32], and Kuhn [
78]. DESI significantly and positively influences SDGs aimed at poverty eradication and health promotion, findings that support those of Bachmann et al. [
17]. These results suggest that the adoption and development of digital technologies could contribute to poverty reduction and improve the health and well-being of the population [
68]. However, no significant influences were found regarding SDG2. This result may indicate that the impact of digital technologies on addressing hunger issues may be limited or influenced by other specific factors and contexts [
32,
36].
The integration of advanced technologies into production and distribution processes brings significant benefits, improving the efficiency, quality, and sustainability of the entire food production chain [
102]. Furthermore, bringing innovation and digital transformation into the food sector can contribute to economic growth and sustainable development of communities, offering opportunities for farmers, producers, and consumers alike [
103]. However, it is essential to recognize that the adoption process of digital technologies may differ depending on the economic, social, and cultural context of each country [
26]. Therefore, it is essential to understand the particularities and specific needs of each environment where these technologies are implemented to ensure an efficient and fair transition to a more innovative and sustainable agriculture and food industry [
32].
The results of the cluster analysis show a significant correspondence between the performance of countries regarding the digital economy and their progress in achieving SDGs related to food, validating Hypothesis H2 in line with the findings of previous research [
6,
12,
13,
59]. This finding emphasizes the importance of adopting and implementing digital technologies to promote a more sustainable agriculture and food industry and contribute to achieving SDGs globally.
Despite global expansion, IT infrastructures are still inadequate in many regions, particularly in developing countries [
17]. The rise in inequality affects emerging and developing countries because they do not benefit from the implementation of digital technologies. The high upfront costs associated with implementing robotic systems can pose a barrier to entry, particularly for small and medium-sized family farms. This discrepancy in access to advanced technologies could widen the gap between larger, more financially secure farms and smaller, resource-constrained operations. This digital divide can aggravate global divisions and hinder equal access to the benefits of technology. By acknowledging these disparities, policymakers and stakeholders can work towards devising strategies to ensure equitable access to technology and mitigate the risk of widening inequality within the agricultural sector. The results of the cluster analysis conducted among EU countries, including cluster C (such as Romania and Bulgaria), revealed a negative influence of low levels of digitization on high levels of inequality. A balanced and collaborative approach is necessary to address these issues and promote a more equitable and sustainable technological development globally [
32].
Therefore, it is evident that the advancement of digital technology is a critical factor in strengthening the resilience of the food system and promoting a more sustainable and adaptable agriculture to changes in the surrounding environment and market demands [
104,
105,
106]. Governments, the private sector, and other stakeholders must continue to invest in the development and implementation of digital technologies in agriculture and food to ensure a safer and more prosperous future for all involved in the food chain [
107].
5.1. Theoretical Implications
Improving and transforming food processing systems are essential to meet increasing demand and achieve sustainability and resilience goals in the food industry. Deep collaboration among industry stakeholders, researchers, and government entities is imperative for the advancement and adoption of cutting-edge technologies and methodologies in food processing. Innovations like automation and connectivity play pivotal roles in tackling the escalating challenges within the food sector, offering ways of creating heightened efficiency, accuracy, and sustainability in food processing operations. These technological breakthroughs hold the potential to bolster food safety measures, elevate product standards, and diminish food loss and waste, thereby facilitating a more resilient and resource-efficient food industry.
The integration of digital technologies into the agricultural sector brings multiple benefits, ranging from improving operational efficiency and increasing productivity to reducing negative environmental impact and promoting more sustainable agriculture. These technologies can help farmers manage resources more efficiently, optimize production processes, and reduce dependence on external inputs, thereby contributing to more efficient use of natural resources. Implementing digital technologies can improve access to information and services in rural areas, thereby reducing disparities and promoting equitable development in the agricultural sector.
The research results underscore the importance of continuing research efforts and implementing digital technologies in the food sector, with particular attention to how these technologies can contribute to achieving SDGs. It is crucial to continue investigating and exploring how digital technologies can be integrated and efficiently utilized to address the complex challenges related to food and promote a fairer, more sustainable, and more resilient food system.
5.2. Empirical Implications
The evolution of digital technology in the food sector brings significant benefits not only in terms of efficiency and productivity but also in enhancing the adaptability and resilience of the entire food system. Digital technologies enable process optimization and waste reduction, thereby enhancing the sustainability and efficiency of the entire food supply chain. This study underscores the significance of precisely evaluating the digital economy’s impact on the sustainable evolution of the food system. By examining the influence of the digital economy illustrated by DESI on key food-related SDGs, namely SDG 1, SDG 2, SDG 3, and SDG 10, the research highlights a clear correlation between the advancement of the digital economy and the strides made toward achieving these crucial sustainable development objectives.
The research results show that DESI has mixed effects on food-related SDGs (SDG1, SDG2, SDG3, and SDG10). While significant influences of DESI are observed on SDG1, SDG3, and SDG10, it seems to have a smaller or nonsignificant impact on SDG2. It is essential to understand that digital transformation, although it may have a positive impact on specific aspects of SDGs, is not a panacea and cannot solve all issues related to sustainable development in the food domain. Thus, countries’ governments need to be aware of this complexity and adopt well-adjusted, multidimensional approaches to endorse sustainable development. Furthermore, these results suggest that further analysis is needed to understand better how the digital economy can contribute to achieving food-related SDGs.
Understanding how digital technologies can contribute to achieving these objectives enables policymakers to develop and implement more effective strategies (policy interventions, investments in digital infrastructure, and capacity-building initiatives) to address complex challenges related to food security, poverty, nutrition, and inequality. Policy interventions encompass regulatory measures and governmental actions aimed at addressing specific issues or achieving particular goals. In the context of enhancing food security through digital transformation, policy interventions might involve implementing regulations to ensure equitable access to digital technologies, incentivizing the development and adoption of digital tools for agriculture and food distribution, and establishing frameworks for data governance and privacy protection.
Investments in digital infrastructure refer to financial commitments made by governments, private sector entities, or international organizations to improve the technological foundation necessary for digital transformation. These investments could include funding for expanding broadband internet access to rural and underserved areas, establishing digital platforms for farmers to access market information and financial services, and supporting research and development in agri-tech innovations.
Capacity-building initiatives involve activities aimed at enhancing the knowledge, skills, and capabilities of individuals, organizations, and communities to effectively utilize digital technologies for improving food security outcomes. These initiatives may include training programs for farmers and agricultural extension workers on how to use digital tools for crop management, supply chain optimization, and market access. Additionally, capacity-building efforts could focus on building the technical expertise of government agencies and non-governmental organizations to design and implement digital solutions that address the unique needs and challenges of different regions and communities.
5.3. Limitations and Further Research
Although this research makes significant contributions to understanding the relationship between digital transformation and food security, there are several limitations to consider. This research is based on DESI to measure digital transformation. However, other dimensions of digital transformation could be included in the analysis to obtain a more comprehensive picture. Also, generalizing the results would require further investigation outside the European Union to confirm and validate the findings. Alternative research methodologies could also be examined to complement and validate the results obtained in this study. Using more up-to-date and comprehensive data could strengthen the robustness of the analysis.
Concerning further research, contextual analysis of the influence of socio-economic and cultural factors, evaluating the impact of policy interventions, and extending the analysis could provide a more comprehensive and detailed perspective on this complex relationship. Using diverse research methods, such as mixed approaches or case studies, could also contribute to a deeper understanding of the phenomenon and provide richer data and information.
In addition to addressing the four food-related SDGs (SDG1, SDG2, SDG3, and SDG10), integrating insights from other SDGs, such as SDG12, focusing on sustainable consumption and production patterns, is crucial. Promoting responsible production practices, including waste reduction and minimizing environmental impact, empowers the agri-food sector to significantly contribute to achieving SDG12 targets by implementing efficient production methods and adopting eco-friendly technologies. Addressing food waste is paramount for mitigating hunger, enhancing food security, and ensuring food safety, requiring measures such as improving infrastructure, implementing better inventory management, and raising consumer awareness about waste reduction. Integrating responsible production and consumption practices, alongside efforts to reduce food waste, is essential for achieving SDGs related to food security, environmental sustainability, and social equity, enabling the agri-food sector to play a pivotal role in building a more resilient and sustainable food system for the future.