2.1 Factors affecting the digital transition in the agri-food sector

There are various factors that influence the sustainable digital transformation of the agri-food producing companies in Europe. Top management commitment and its initialisation, monitoring, and evaluation of performance, competitive advantages, communication channels, trialability, observability, and usability are summarised by Benzidia et al. (2021) as factors for sustainable digital transition. Managers’ initial knowledge of digitalization also affects how Industry 4.0 trend will impact the entire company and the business model (Müller et al., 2018a).

Some authors suggest that modern management in the digital enterprise is realised through the adaptation of new information systems. Management information systems use new data processing solutions to define strategy and build highly integrative enterprise-wide systems such as client relationship management, enterprise resource planning or others to manage pro-cesses (Davenpport,1998) and create new business models (Johnson, 2018). Corporate structures will only evolve efficiently based on the new computing solutions (Goelzer and Fritzsche, 2017) and reach their full potential if they successfully define the organizational culture that best fits their digital strategy (Martínez-Caro et al., 2020; Papazov and Mihaylova, 2015).

To this end, the existing studies show the specific challenges of the size of the company in relation to Industry 4.0. For SMEs in particular, these challenges include limited resources, low bargaining power and concerns that existing business models may be unsuitable for Industry 4.0 (Müller and Voigt, 2016; Müller et al., 2018b). In addition, SMEs usually have specific characteristics when it comes to the introduction of information technologies in general (Sharma and Bhagwat, 2006). Therefore, SMEs need solutions that are tailored to their specific challenges, but research has mainly focused on large enterprises rather than SMEs (Müller, Buliga and Voigt, 2018). Thirdly, the upper management of SMEs is able to oversee the entire organization.

This process is also part of the so-called “circular economy” (Zhelyazkova, 2017). It represents an emerging economic paradigm that seeks to replace traditional linear production models. It emphasizes reducing waste and fundamentally rethinking how products are conceived, used, and managed throughout their lifecycle (Jaaron and Backhouse, 2021). This shift is considered a highly ambitious challenge, impacting both production systems and society at large. It necessitates adopting sustainable activities and processes in production and consumption that can efficiently manage the planet’s limited resources (Lardo et al., 2020).

The transition to a circular economy is significantly supported by the development of digital technologies associated with Industry 4.0, also referred to as the Fourth Industrial Revolution. This phase is characterized by a technological mix that includes robotics, sensors, network connectivity, advanced programming, and the Internet of Things (IoT) (Alqahtani et al., 2019).

The synergy between digitalization and the circular economy has the potential to transform the labour market over the long term, influencing both processes and resources as well as the required skills and expertise (Alcayaga et al., 2019). Digitalization plays a pivotal role in developing activities within the circular economy ecosystem. For instance, IoT technologies can monitor product lifecycles, while data analytics can optimize production quantities, ensuring they remain sustainable. By leveraging insights into consumer purchasing and consumption patterns, businesses can better align production with demand, minimizing waste and overproduction (Jabbour et al., 2019), including such digitization can offer insights into the control and supply of goods aligned with national and transnational, financing conditions as part of the green finance agenda (Zhelyazkova and Kitanov, 2015; Ivanov and Werner, 2025).

The rapid pace of digital transformation has significant socioeconomic implications for small and medium-sized industrial organizations. Among the key social challenges are the heightened risk of cybercrime resulting from increased connectivity and job losses driven by the automation of large operational segments across various industries (Stoycheva and Antonova, 2018). While digitalization may create new opportunities for high-skilled workers, the overall volume of such jobs remains limited. In the agri-food sector, SME managers’ intentions to adopt digital models in operations and processes are primarily influenced by factors such as the expected performance of the technologies, their complexity, and the social pressures exerted on them (Petter, 2022).

In smart factories, employees collaborate to share experiences and knowledge, leveraging their strengths to solve problems collectively. Modern technologies support the production of highly personalized, high-quality products. Collaboration, often in virtual teams, becomes the norm, alongside openness to new challenges. Within these teams, roles are distributed to allow members to fully utilize their capabilities while maintaining exceptional customer service and operational performance. Team members trust and support one another, offering advice and confidently delegating tasks, ensuring they are completed to the highest standard (Grabowska, 2020).

The implementation of smart management and automated manufacturing systems — grounded in Big Data, cyber-physical systems, and dynamic production networks — introduces new challenges related to data integrity, privacy, and security (Rajput and Singh, 2019). Cyber-attacks pose significant threats, including potential destruction of equipment, alteration of product designs, or disruption of manufacturing processes, all of which can result in substantial financial losses for factories (Elhabashy et al., 2019; Vitliemov et al., 2020).

Key components of Industry 4.0 include cyber-physical systems, the Internet of Things (IoT), cloud manufacturing, and additive manufacturing. However, Industry 4.0 remains a broad and complex concept that can be challenging for organizations to implement effectively. Strong managerial leadership is essential, yet the process becomes more feasible when guided by a well-defined strategy or business model that aligns with digital and green transformation goals. Drawing on theoretical insights, this study identifies six critical success factors from the management literature that have the most significant impact on the sustainable implementation of Industry 4.0: (1) strong leadership, (2) an organizational culture that embraces change and adaptation, (3) alignment with the initial level of digitalization, (4) flexibility and adaptability, (5) consideration of external environmental factors, and (6) robust cybersecurity measures (see Varbanova et al., 2023).

2.2 Adapting to disruption: the role of Industry 4.0 in business model transformation

The rapid digitalization of the global business environment is dismantling traditional industry barriers, prompting academics and practitioners to call for a reevaluation of existing business models (BMs). Adapting and evolving BMs to remain competitive has emerged as a key challenge in the face of disruptive technologies (Rachinger et al., 2019; Stock and Seliger, 2016) and the ongoing crises in Europe (Agostini and Nosella, 2021). For instance, Clauss et al. (2022) demonstrated how SMEs responded to the COVID-19 crisis through temporary business model innovation. By conducting a multiple case study of five SMEs in Austria, Germany, and Liechtenstein, they identified this form of innovation as a strategy that generates additional value and opens new revenue streams for firms.

The concept of business models is fundamentally linked to the identification and exploitation of opportunities (DaSilva and Trkman, 2014; George and Bock, 2011), particularly those introduced by emerging technologies (Chesbrough, 2010; Sabatier et al., 2012; Spieth and Schneider, 2016; Zott et al., 2011). Business models provide a framework for understanding how organizations can leverage Industry 4.0 technologies to deliver appropriate value propositions and develop effective pricing models.

As defined by Osterwalder and Pigneur (2010, p.14), a business model describes “the rationale of how an organization creates, delivers, and captures value.” This definition highlights three interconnected dimensions:

• (1) Value creation, encompassing key activities, resources, and partnerships.

• (2) Value delivery, including the products and services offered, distribution channels, customer segments, and customer relationships.

• (3) Value capture, which focuses on the firm’s cost structures and revenue streams.

Traditional business models often integrate agile and lean methodologies, particularly in the context of digital start-ups, to reduce uncertainty and facilitate experimentation, development, and testing of business concepts (Carroll and Casselman, 2019; Ghezzi and Cavallo, 2020). For established companies in traditional industries struggling to innovate, alternative frameworks have been proposed to support the transformation of their business models. For example, Warner and Wager (2019) introduced a process model incorporating external triggers, key enablers, and key barriers to build dynamic capabilities for digital transformation. Similarly, Lichtenthaler (2020) and Hanafizadeh et al. (2018) have explored strategies tailored for traditional companies to foster innovation in their business models.

The transition to new business models in the context of Industry 4.0 requires overcoming traditional frameworks like the automation pyramid in favour of decentralized models with interconnected systems (Monostori, 2014). Kagermann et al. (2013) describe the new Industry 4.0 paradigm as being grounded in three core features:

• (1) Horizontal integration through value networks, enabling intelligent supply chains that synchronize suppliers and customers beyond the factory while integrating internal value chains from engineering to sales.

• (2) Vertical integration via networked manufacturing systems, wherein IT infrastructures allow data to flow seamlessly between automation systems and enterprise resource planning (ERP) systems, facilitating feedback and corrective actions.

• (3) End-to-end digital integration across the value chain, which supports seamless communication throughout the value creation process, from product and process development to maintenance and recycling.

These integrations enhance efficiency and flexibility, enabling firms to adapt to dynamic market conditions and capitalize on opportunities presented by digitalization. By adopting these innovative models, businesses can better align their operations with the demands of a rapidly evolving, digitally-driven global economy.

While business model research has extensively explored the possibilities and impacts of digital technologies, the specific influence of Industry 4.0 – and particularly Agriculture 4.0 – on business models remains underexplored (Arnold et al., 2016). Most empirical studies addressing the Industrial Internet of Things (IoT) have largely overlooked the unique challenges and opportunities faced by SMEs, especially in less economically developed regions such as Eastern Europe (Arnold et al., 2016; Echterfeld et al., 2016; Kiel et al., 2017a,b; Müller et al., 2018a; Ivanov, 2025).

2.3 From Innovation to monetization: crafting business models for the smart factory era

Recent research has predominantly emphasized technological advancements, often sidelining the exploration of new BMs arising from the integration of these innovations. However, the emerging industrial paradigm of Industry 4.0 is profoundly altering traditional value-creation processes (Chkarat et al., 2023). This transformation entails significant shifts in technological and production practices (Annosi et al., 2020), fostering far-reaching organizational changes and creating opportunities such as enhanced collaboration, improved customer relationships, and innovative product and service offerings (Pellegrini et al., 2023). Consequently, there is a growing imperative for the development and adaptation of business models to harness these opportunities effectively.

The enabling technologies of Industry 4.0 compel and empower firms to rethink and potentially overhaul their existing BMs, presenting unprecedented competitive advantages (Ciampi et al., 2021; Del Sarto et al., 2021; Dijkman et al., 2015). Experimentation with emerging technologies, coupled with insights into evolving global market needs, naturally leads to innovative BM approaches (Morrison and Rabellotti, 2017). This has led many scholars to champion the role of Industry 4.0 technologies in driving business model innovation (BMI) (Chen et al., 2021). BMI becomes even more relevant when contextualized within shifting consumer demands (Brannon, 2016).

In this context, the business model of the “new era of innovation” integrates social and technical architectures, bridged through business processes. Social architecture encompasses resources such as knowledge, management systems, employee competencies, and motivational mechanisms. Technical architecture involves the infrastructure, including IT and telecommunication systems, machines, and other technologies. Business processes serve as the dynamic interface, combining these foundational elements to generate the resources necessary for delivering customer-centric products that add value (Grabowska et al., 2020).

The smart factory epitomizes this model. It promotes knowledge-sharing among employees, leverages collective strengths for problem-solving, and utilizes modern technologies to produce highly personalized, premium-quality goods. This evolving BM framework provides insights into how companies can effectively utilize Industry 4.0 technologies to offer compelling value propositions and adopt innovative pricing models. Yet, the implications of these changes for the workforce — both in terms of tasks and required skills — demand further exploration.

Employment, a critical concern of the fourth industrial revolution, is undergoing significant transformations. The adoption of smart technologies necessitates new competencies for both direct and indirect workers. While operators must familiarize themselves with advanced devices, managerial roles increasingly require proficiency with IT tools for data analysis. These shifts underscore the need for comprehensive workforce upskilling to align with Industry 4.0 imperatives.

Finally, the concept of value capture, often termed monetization, plays a pivotal role in sustaining businesses through commercial activity. Value capture refers to the mechanisms by which a company is compensated by customers, ensuring its financial viability (Baden-Fuller and Haefliger, 2013; Sosna et al., 2010; Teece, 2010) or what can also be termed financialization of the wider economy (Ivanov, 2022). By integrating these considerations, this study aims to propose a comprehensive model that bridges the technological advancements of Industry 4.0 with the evolving needs of businesses and their stakeholders, particularly in the context of SMEs.

This paper addresses these gaps by adopting a business model perspective to investigate how agri-food manufacturing SMEs are adapting to the demands of Industry 4.0. Business models, as conceptualized in the literature, articulate how organizations structure their activities to deliver value to customers (Chesbrough and Rosenbloom, 2002; Taran et al., 2015; Zott et al., 2011), interact with suppliers, partners, and clients (Bouncken and Fredrich, 2015; Ng et al., 2013), and monetize their products and services (Massa et al., 2017).

The novel contribution of this study lies in proposing the innovative concept of “digitainability” as discussed in sub-section 2.3. In short, the advanced model integrates the principles of digital transformation with the fundamentals of traditional business models to address sustainability. By doing so, the paper introduces a framework tailored to the unique contexts of SMEs operating in economically constrained regions. This approach not only fills a critical research gap but also offers actionable insights for policymakers and practitioners seeking to navigate the complexities of digital and sustainable transitions, as discussed.

2.4 Research framework: towards leveraging Industry 4.0 for Agriculture 4.0

Agriculture, traditionally one of the least digitalized industries, is increasingly recognizing the transformative potential of Industry 4.0 technologies (Gandhi et al., 2016). While the origins of Industry 4.0 lie outside the agricultural sector, its advanced manufacturing methods offer unprecedented opportunities to address critical challenges such as efficiency gains, maximizing yields, minimizing environmental impact, improving quality, and adapting to climate change (Chandes and Estampe, 2003; Forsman-Hugg et al., 2013; Garcia et al., 2012). Despite this potential, many agri-food industries lag in adopting collaborative innovations, even as external pressures, such as rapid climate change, demand significant transformations (Doloreux et al., 2013).

The urgency of these challenges is evident in the sector’s need for innovation, particularly as climate change intensifies its impact on agricultural activities. Literature on Industry 4.0 in agriculture highlights various models and frameworks aimed at facilitating this transformation. These models address key decisions for integrating new technologies, such as determining when to acquire new equipment and processes or modernize existing systems to align with Industry 4.0 standards (Kolla et al. 2019; Medić et al. 2016).

A notable contribution is the Technology Acceptance Model (TAM) 4.0, which supports management teams in evaluating the integration of Industry 4.0 technologies. This model, proposed by Medić et al., provides a structured approach to decision-making about equipment upgrades, process modernization, and the adoption of digital tools. Additionally, Mitidiero et al. (2023) expand this framework by incorporating a retrofit approach, emphasizing how existing systems can be adapted for Industry 4.0 compatibility.

The present paper builds on the TAM 4.0 framework, adapting it specifically for the digital transformation of SMEs in the agri-food sector. The proposed model integrates multiple critical factors influencing management decisions, including:

• (1) Ease of use and perceived usefulness of the technology for the organization and its leadership.

• (2) The impact on consumers and employees, focusing on how the technology influences experiences and workflows.

• (3) Evaluation of financial performance, ensuring economic viability for SMEs.

• (4) Consideration of environmental performance, aligning with sustainability goals.

This comprehensive adaptation ensures the model not only guides technology adoption but also addresses the unique challenges and opportunities faced by agri-food SMEs transitioning into the Industry 4.0 era.

The proposed model incorporates these factors into a cohesive framework (see Figure 1), offering a roadmap for SMEs to navigate the complexities of digital transformation. By bridging technological innovation with practical implementation strategies, the model empowers management teams to make informed decisions that balance organizational, consumer, and environmental priorities. This approach underscores the critical role of Industry 4.0 in shaping the future of agriculture, transforming it into a more resilient, sustainable, and adaptive industry.

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New suggested model for Industry 4.0 adoption for industrial SME in Europe.

Citation: International Food and Agribusiness Management Review 28, 3 (2025) ; 10.22434/ifamr.1267

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Business Model Innovation (BMI) unfolds in a structured, phased approach, aligning closely with theoretical perspectives on the intersection of Industry 4.0 technologies and organizational objectives (Ciampi et al., 2021; Müller et al., 2018). It integrates core principles of the Technology Acceptance Model (TAM) 4.0 — notably, perceived usefulness and ease of use — to ensure its relevance to manufacturing SMEs undergoing digital transformation.

Therefore, first phase of our research framework focuses on defining the organization’s overarching objectives, followed by specific manufacturing (or operational) goals. This aligns with Chesbrough and Rosenbloom’s (2002) framework for value creation and captures the interplay between strategic goals and operational necessities. The second phase evaluates digital transformation impact factors, reflecting on insights from prior studies emphasizing the critical role of Industry 4.0 enabling technologies in transforming business models (Kagermann et al., 2013; Chen et al., 2021).

Subsequently, the third phase assesses existing manufacturing equipment and processes, identifying whether they require modernization or retrofitting. This stage emphasizes practicality by focusing on achieving operational objectives through targeted changes in infrastructure, a key consideration in Mitidiero et al.’s (2023) retrofit approach.

The fourth phase introduces a detailed analysis of Industry 4.0 technologies applicable to manufacturing processes. This stage involves a comparative assessment of each technology, considering its advantages, disadvantages, and contribution toward achieving defined manufacturing goals. This emphasis on the systematic evaluation of technologies is consistent with frameworks outlined in Müller et al. (2018a) for fostering organizational alignment with Industry 4.0 objectives.

Finally, the fifth phase synthesizes findings into a comprehensive summary table, evaluating the financial, social, environmental, and automation-related effects for the organization. This stage aligns with contemporary calls for a holistic assessment of Industry 4.0 impacts, incorporating dimensions of sustainability and stakeholder value (Baden-Fuller and Haefliger, 2013; Taran et al., 2015).

By integrating theoretical constructs such as TAM 4.0 and leveraging insights from models of business process innovation (Grabowska et al., 2020), this phased approach provides a practical framework for SMEs. It aligns closely with existing literature while addressing gaps in studies focused on digital transformation in less-developed regions or specific industries like agriculture (Arnold et al., 2016; Gandhi et al., 2016). Ultimately, this model serves as a roadmap for businesses navigating the complexities of Industry 4.0, fostering informed decision-making and innovative practices in BMI.

3.1 Data collection

To capture a comprehensive understanding of how Industry 4.0 technologies are adopted within SMEs, data collection utilized a multi-method approach over a six-month period. Semi-structured interviews were conducted with five key stakeholders across various organizational levels, ensuring a diverse range of perspectives (see Table 1). These interviews were designed around a framework that covered critical themes such as digital adoption, managerial challenges, operational changes, and perceived impacts on organizational performance.

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Characterization of the experts in Company A

Citation: International Food and Agribusiness Management Review 28, 3 (2025) ; 10.22434/ifamr.1267

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Supplementary data included internal documents, operational records, and field observations, which provided additional context and helped validate the findings from the interviews. This triangulation of data sources aligns with recommendations by Gabriel et al. (2023), who advocate for multi-stakeholder engagement to ensure a holistic understanding of organizational transformations. For instance, insights from the company’s owner and CEO provided strategic perspectives, while input from the accountant and Head of Manufacturing highlighted operational and financial implications.

Such an approach is particularly relevant in studying SMEs, where decision-making often involves multiple layers of management and where stakeholder perspectives can significantly influence the trajectory of digital transformation. By incorporating diverse viewpoints, the study sheds light on both the opportunities and challenges associated with integrating Industry 4.0 technologies. All of the experts were involved in the process of digitalization.

3.2 Data coding

The study employed thematic analysis to systematically identify patterns and themes within the collected data. Using NVivo software, the data were coded according to a framework informed by key constructs from the literature. These included:

• (1) Initial knowledge and understanding: examining how Industry 4.0 concepts were introduced to the company and the level of awareness among stakeholders (Kagermann et al., 2013).

• (2) Development and implementation: assessing how the company operationalized digital transformation within its farming and manufacturing processes (Frazzon et al., 2013).

• (3) Data exploitation: analyzing how the company leveraged new data streams for decision-making and performance improvement.

• (4) Internal and external effects: exploring the organizational, social, and economic impacts of Industry 4.0 adoption.

• (5) Outcomes and barriers: identifying factors that facilitated or hindered the company’s digital transformation (Balocco et al., 2012; Gatautis et al., 2019).

The use of NVivo allowed for the systematic organization and analysis of qualitative data, enabling the identification of recurring themes and the relationships between them. This structured approach ensured that the findings were grounded in the data while also connected to broader theoretical frameworks.

By employing this coding process, the study contributes to the understanding of how SMEs can effectively navigate through their digital transformation. The analysis also highlights the importance of considering organizational culture, stakeholder engagement, and external environmental factors in the adoption of Industry 4.0 technologies.

3.3 Data analysis

To explore how SMEs like Company A adapt to Industry 4.0, a thematic analysis was conducted on data collected through semi-structured interviews and document reviews. Using NVivo software, qualitative data were organized and coded into specific themes: initial understanding of Industry 4.0 concepts, implementation in agricultural and manufacturing processes, data exploitation strategies, and the social, economic, and environmental outcomes of digital transformation.

The interviews were conducted following a structured methodology, beginning with an introduction to the Industry 4.0 concept and its theoretical foundations, followed by a detailed discussion of the proposed model for digital transformation. Each interview phase evaluated the company’s objectives, the contextual factors influencing their Industry 4.0 journey, and the perceived impacts on operational and strategic levels.

Coding efforts revealed that Company A has leveraged Industry 4.0 technologies effectively in its manufacturing processes, despite certain challenges. Initial knowledge acquisition about Industry 4.0 involved both internal and external research. The management team demonstrated foresight by adopting advanced technologies that not only optimized production processes but also enhanced product quality and reduced waste.

Key drivers supporting the company’s digital transition included a proactive managerial vision, investment in cutting-edge technologies, and a strong commitment to employee training and development. Employees at all levels were engaged in the transformation process, fostering a culture of adaptability and innovation. These findings align with existing studies emphasizing the role of management and workforce readiness in Industry 4.0 adoption (Balocco et al., 2012; Gatautis et al., 2019).

Barriers to transformation were also identified. These included financial constraints, skill gaps among employees, and the need to overcome traditional mindsets within the organization. Nevertheless, the company has developed strategic responses to these challenges, such as targeted training programs and phased implementation plans.

The outcomes of Industry 4.0 adoption at Company A are multifaceted. Economically, the company has enhanced its productivity and competitiveness. Socially, it has invested in workforce upskilling, ensuring that employees remain integral to the digital transformation process. Environmentally, the advanced processing methods have reduced energy consumption and preserved natural resources, contributing to the company’s sustainability goals.

The insights gained from this case study contribute to a broader understanding of how SMEs in the agri-food sector can navigate the complexities of digital transformation. By addressing both drivers and barriers, “Company A” exemplifies how an integrated approach to Industry 4.0 can deliver tangible benefits across financial, social, and environmental dimensions. This analysis underscores the critical role of strategic planning and stakeholder engagement in achieving successful business model innovation in SMEs.

4.1 Phase 1: Identifying overarching objectives

The first phase of the model involved identifying and defining the company’s overarching objectives, followed by those specific to manufacturing operations. In this case study, Company A set a strategic goal to modernize its operations, expand production capacity, and align with global trends in grain processing. To achieve this, the company engaged in benchmarking by visiting similar facilities in Europe and the United States to gain insights into cutting-edge technologies and their production impact.

Initially, the company relied on both savings and borrowed capital to invest in expensive new technologies. Through collaboration with the research team, Company A developed a comprehensive four-year strategy to digitize its business. This plan included:

• Implementing a custom-developed management system to collect and analyze data related to production processes.

• Establishing an in-house laboratory to monitor production quality.

• Investing in key advanced machinery to enhance operational efficiency.

4.2 Phase 2: Impact factors for digital transformation

The second phase examined the factors influencing the company’s digital transformation and involves an analysis of existing equipment and manufacturing processes. For Company A, the primary drivers of transformation were the need to close the production lifecycle and diversify the business model.

Key theoretical factors identified in the literature played a role, including the initial knowledge and experience of Industry 4.0 among top management, which proved to be pivotal for the transition. Environmental conditions also had a significant impact. Traditional alfalfa production involves drying crops under natural atmospheric conditions, a process that is time-consuming and weather-dependent. In contrast, modern technologies allow for faster and resource-efficient processing under any weather conditions.

These environmental considerations were equally relevant to the company’s farming activities, even though this study primarily focuses on manufacturing. Drought and water scarcity in the region directly impact production, as the plant relies on approximately 80% of its own alfalfa production.

Additional external factors influencing digital transformation included:

• EU and national directives promoting digital and green transitions.

• Geopolitical shifts, such as changes in global markets driven by war and instability.

• Workforce shortages in remote agricultural areas, which posed significant operational challenges.

4.3 Phase 3: Overcoming perceived ease of use challenges

While the management team at Company A clearly recognized the perceived usefulness of Industry 4.0 technologies, the perceived ease of use presented challenges. The transition involved a steep learning curve as employees adapted to new machinery and software systems. The company experienced mistakes and financial losses during this adjustment period.

To address these challenges, Company A implemented workforce training programs and focused on retaining personnel year-round rather than only during the harvesting season. This commitment to employee development was critical to overcoming operational disruptions and fully realizing the benefits of digital transformation.

4.4 Phases 4 and 5: implementing and evaluating technologies

In the final phases, the model evaluates how new technologies support the integration of Industry 4.0 into existing manufacturing processes to achieve financial, social, environmental, and customer-centric objectives.

The financial data (Table 2) demonstrate a marked increase in investments in machinery and digital assets over recent years. These investments have driven significant advancements in efficiency and quality. However, external disruptions — such as supply chain interruptions during the COVID-19 pandemic and geopolitical instability from the war in Ukraine — have negatively affected grain markets in the region. As a result, the company has temporarily scaled back some of its initial plans for expanding the use of digital tools in its manufacturing operations.

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Financial and economic data for Company A (2020–2023)

Citation: International Food and Agribusiness Management Review 28, 3 (2025) ; 10.22434/ifamr.1267

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Company A has leveraged digital transformation and technological advancements to address challenges posed by a volatile market environment and climate change. These external pressures significantly impact the company’s operations, particularly in the agri-food sector, which is heavily reliant on stable environmental conditions. By adopting an innovative business model, the company emphasizes the social dimension of digital sustainability, prioritizing workforce retention and continuous investment in digital skills and knowledge. This focus is particularly relevant for remote agricultural regions, where attracting and retaining skilled labour is a significant challenge.

The company’s strategic decisions are supported by a data-driven approach to operations management and sustained investment in key technological assets. Table 3 illustrates the company’s financial performance from 2020 to 2023, revealing key trends and the impact of digital transformation efforts. The data indicate significant growth in revenue and profitability, particularly during 2022, where digital asset investments yielded a high return on investment (ROI) of 128.95%. However, the market volatility caused by external factors such as global market shifts and geopolitical instability, alongside increasing materials costs as a percentage of revenue, underscores the challenges faced by the company.

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Financial data, YoY changes (2020–2023)

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The digital transformation journey at our case study includes several strategic actions designed to modernize operations and enhance sustainability. These are delineated below.

• (1) Implementation of management systems for data analysis: Company A has developed systems to analyze production data comprehensively, providing actionable insights for process optimization. This has helped the company respond more effectively to challenges related to efficiency and cost management.

• (2) Investments in machinery and fixed assets: over recent years, the company has made substantial investments in modern machinery and fixed assets to improve production efficiency. This is reflected in the high ROI for digital assets in 2022 and the sustained growth in investment in fixed assets through 2023.

• (3) Environmental and external considerations: the environmental conditions required for alfalfa production, such as drying and water availability, play a central role in the company’s digital strategy. The use of modern technologies has reduced reliance on favorable weather conditions, thereby increasing resilience. Additionally, EU directives, global market shifts, and workforce shortages in rural areas have informed strategic decisions, underscoring the importance of aligning digital initiatives with external trends.

• (4) Challenges in adoption and ease of use: despite recognizing the perceived usefulness of Industry 4.0 technologies, the company faces significant challenges in achieving ease of use. Employees experienced a steep learning curve when adapting to new machinery and software, which led to operational mistakes and financial losses. Workforce training and retention, particularly in agricultural regions with seasonal employment patterns, remain ongoing challenges.

• (5) Mitigation of market and climate risks: digital transformation has proven instrumental in mitigating the negative effects of market instability and climate change. By adopting more efficient and automated processes, the company has managed to stabilize operations and maintain competitiveness despite increasing materials costs and other external pressures.

Insights gathered from focus group discussions (Table 4) highlight opportunities to refine and enhance the model for implementing Industry 4.0 in similar SMEs:

• (1) Prioritizing workforce development and retention to ensure the long-term sustainability of digital initiatives.

• (2) Enhancing the accessibility and usability of digital tools to reduce barriers to adoption among employees.

• (3) Leveraging collaborative networks and partnerships to share best practices and technologies across the industry.

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Focus group insights on employee perceptions of Industry 4.0: impact, adoption, and areas for improvement

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Through its efforts, Company A serves as a compelling case study for the adoption of Industry 4.0 technologies in the agri-food sector. The company’s experience underscores the importance of aligning digital transformation strategies with environmental, social, and market realities to achieve sustainable growth.There is a significant disparity in understanding of Industry 4.0 across different organizational levels. While top management (Owner and CEO) are very familiar or familiar with the concept, lower-level managers have limited familiarity. This suggests a need for comprehensive education and training programs within the organization. The relevance of Industry 4.0 is not uniformly recognized across the organization. This indicates a need for better communication of the potential benefits and impacts of Industry 4.0 throughout the company. Each department anticipates different changes from Industry 4.0 implementation, ranging from enhanced product quality to improved R&D processes. This highlights the importance of a holistic approach to Industry 4.0 implementation that addresses various aspects of the business. There is no consensus on whether to adopt an entirely new business model or extend the current one. This suggests a need for a flexible, phased approach to business model innovation that can accommodate different departmental needs and readiness levels. The drivers for change are split between internal and market factors. This indicates the need for a balanced approach that considers both internal capabilities and external market pressures. Different departments identify distinct areas for improvement through Industry 4.0, from speed and flexibility to automation and data analytics. This underscores the need for a comprehensive implementation strategy that addresses multiple operational aspects. However, all respondents acknowledge the relevance of Industry 4.0 to customers and competitors, indicating a strong motivation for adoption. The identified trends range from sustainability to personalized products, suggesting the need for a multi-faceted approach to Industry 4.0 implementation.

The implications of this study extend beyond individual firms; they resonate throughout the broader agricultural sector and regional economies within Eastern Europe. By highlighting the critical need for targeted interventions aimed at facilitating successful transitions toward embracing digitized frameworks, this research underscores the importance of developing tailored strategies that consider both technological advancements and organizational culture.

For managers in agri-food SMEs, this study provides actionable insights into how they can leverage Industry 4.0 technologies to enhance their operations while simultaneously addressing sustainability concerns. Understanding the factors influencing digital transition allows managers to prioritize investments in technology and training that align with their strategic objectives. Furthermore, recognizing the differences between traditional management practices and new business models equips them with the knowledge necessary to adapt their approaches effectively.

However, it is essential to acknowledge limitations inherent in this study. The case study focuses primarily on a single company in Bulgaria; thus, findings may not be universally applicable across all agri-food SMEs in Eastern Europe or other regions facing different socio-economic contexts. Additionally, while qualitative data provides rich insights into managerial perspectives and operational practices, quantitative measures could further validate these findings through broader statistical analysis.

From a policy perspective, this research emphasizes the need for governmental support in fostering an environment conducive to digital transformation within the agricultural sector. Policymakers should consider implementing initiatives that provide financial assistance for technology adoption, facilitate knowledge-sharing platforms among SMEs, and promote educational programs aimed at enhancing digital skills among employees. Moreover, this study contributes to academic discourse by bridging gaps between theory and practice regarding digital transformation in agriculture. It encourages future research endeavors focused on exploring the long-term impacts of Agriculture 4.0 adoption on firm performance and sustainability outcomes across diverse contexts.