Critical Success Factors for Digitalisation in the Circular Economy Transition for Agri-Food SMEs: An SF-AHP Approach

Abstract

While the circular economy offers a new perspective for achieving sustainability goals, digital technologies have become key enablers of this transformation. However, few studies in the literature address the identification and prioritisation of critical success factors for digitalisation that support the transition to a circular economy, particularly for agri-food SMEs operating in developing countries. This study proposes an integrated PESTEL-based and Spherical Fuzzy AHP (SF-AHP) framework to identify and prioritise critical success factors for digitalisation in the circular economy transition of agri-food SMEs. First, the literature-derived critical success factors were identified and structured according to the PESTEL framework. The TOE framework was then employed as a theoretical lens to interpret these factors at the firm level in terms of technological, organisational, and environmental dimensions. A five-member expert panel evaluated the factors in the context of Türkiye, and their relative importance was analysed using a weighted SF-AHP approach. Quantitative results reveal that ‘Data analytics to boost agricultural output’ is the most significant factor (w = 0.128), followed by ‘High investment costs’ (w = 0.123) and ‘Efficient technology for the CE process’ (w = 0.114). To ensure the robustness of the findings, a comparative analysis was performed; the results revealed a strong alignment between SF-AHP and Fuzzy AHP (r = 0.986), as well as a high degree of consistency with AHP (r = 0.910), validating the methodological stability of the proposed framework. This study contributes to the identification of strategic priorities for digitalisation in the transition to a circular economy among agri-food SMEs in developing countries and provides policymakers and practitioners with a guiding framework.

1. Introduction

The rapidly growing global population, climate change, excessive exploitation of natural resources, geopolitical factors, and health crises have recently driven countries and companies away from the linear economic system towards the circular economy (CE) [1,2]. The CE is a sustainability-oriented production model that seeks to redesign current production and consumption models as well as material flows. Its primary objectives are to minimise waste, ensure the efficient use of resources, and keep materials circulating within the economy for extended periods [3,4].

The agri-food sector stands out as one of the most critical areas, as it ensures the continuity of food supply while simultaneously contributing to unsustainable resource use and waste generation [5,6]. The current agri-food system generates approximately one-third of global greenhouse gas emissions and causes 80% of deforestation [2,5]. Furthermore, unsustainable agricultural practices deplete freshwater resources and are a primary driver of biodiversity loss. Current data indicate that over 24,000 species are at risk of extinction as a result of these processes [7], with the global population expected to reach 10 billion by 2050. Food production must therefore increase by 70% to meet demand [8]. However, without technological innovation, this increase is projected to amplify environmental impacts by 50–90%, thus exceeding planetary boundaries [8,9,10].

In light of this urgency, the United Nations’ 2030 Agenda prioritises the transformation of systems in line with the Sustainable Development Goals (SDGs), particularly ‘Industry, Innovation and Infrastructure’ (SDG 9) and ‘Responsible Consumption and Production’ (SDG 12) [9,11,12]. As agriculture is the largest user of water worldwide—with irrigation claiming close to 70% of all freshwater—achieving SDG 12 requires an urgent reduction in the ecological footprint by changing production patterns [13]. Within this framework, the CE transition serves as a strategic mandate to halve global food waste and create resource-efficient supply chains [13,14,15].

In this transformation process, digitalisation is emerging as a crucial enabler of the transition to the CE. Artificial intelligence (AI), robotics, the Internet of Things (IoT), blockchain, and cyber-physical systems support circular applications by making production processes more flexible, traceable, and optimizable [12,16,17,18]. Digitalisation allows for the precise management of critical inputs such as water, energy, and fertilisers, and enables early detection of food losses, which can then be integrated into reuse and recycling processes [19,20,21,22]. Recent research indicates that AI-driven open innovation acts as a transformative force in advancing circular supply chains within the food sector [23] while blockchain technology ensures transparency and traceability across product life cycles [24]. Recent studies also highlight that platform-based digital ecosystems, including live-streaming e-commerce, are increasingly reshaping agri-food supply chains by enhancing cross-border coordination, market access, and sustainability-oriented value creation mechanisms [25].

The Turkish agricultural sector is under significant structural pressure due to climate change, water scarcity, and rising input costs. In response, Türkiye has strategically prioritised alignment with the Paris Agreement and the European Green Deal, as well as the United Nations 2030 Agenda, particularly Sustainable Development Goal 12 on Responsible Consumption and Production. The European Green Deal, developed in line with the European Union’s objective of becoming the first climate-neutral continent by 2050, provides a comprehensive framework encompassing emissions reduction, promotion of the circular economy, zero pollution, transformation of agriculture and rural areas, sustainable transport, energy transition, a just transition, and financing mechanisms for this transformation. This framework applies not only to firms operating within the European Union but also to companies exporting to the EU market. Indeed, approximately 40% of Türkiye’s exports are directed to EU countries [26]. In this context, Turkish firms exporting to the EU are increasingly required to ensure compliance with the Green Deal, together with their supply chain partners. As a significant agri-food exporter in the Europe and Central Asia region, Türkiye remains at an early stage of digital transformation in the agricultural sector, indicating that its digitalisation capacity lags behind the EU average [27,28].

Despite growing interest in the academic literature, the supportive role of digital technologies in CE applications within the agri-food sector has generally been examined in the context of developed countries, specifically focusing on large-scale enterprises and markets with high digital maturity [29,30,31,32,33]. Small and medium-sized enterprises (SMEs) are among the key drivers of economic growth and social progress. However, the question of which digitalisation success factors play a critical role in the transition of SMEs—which constitute a significant portion of the agri-food sector—to a CE has been examined only to a limited extent in the literature [34,35,36,37]. This limitation is particularly important because SMEs constitute the backbone of agri-food systems in many emerging economies, yet often face institutional, technological, and financial barriers that differ substantially from those observed in advanced economies.

Accordingly, this study is guided by the following overarching research question:

RQ: Which critical success factors are most important for the digitalisation of agri-food SMEs during the transition to a CE in developing-country contexts?

To answer this question, the study pursues three objectives:

• To identify the critical success factors influencing digitalisation in the CE transition of agri-food SMEs;
• To determine the importance weights and priority structure of these factors using the SF-AHP method;
• To develop a strategic roadmap for managers and policymakers to accelerate digital circular transformation.

Türkiye provides a particularly relevant empirical context for addressing this gap. As an upper-middle-income country with a population of approximately 84 million, Türkiye continues to assign strategic importance to agriculture within its national economy. Although agriculture’s share of gross national product has declined to approximately 4.8–5.6%, the sector still accounts for nearly 14.8% of total employment and generates agricultural exports exceeding USD 36 billion annually, confirming Türkiye’s strong position within global food supply chains [38]. Nevertheless, this production potential is constrained by a highly fragmented farm structure, with average farm sizes ranging between 6.1 and 7.7 hectares [39]. This fragmented structure limits economies of scale and creates substantial barriers to technological modernisation. Furthermore, the proportion of sustainable agricultural production remains below 2.5%, suggesting that Türkiye faces a widening digital and sustainability gap compared with European agricultural systems [28].

At the same time, Türkiye’s agri-food sector faces increasing pressure from climate change, water scarcity, and rising input costs. In response, the country has begun aligning its agricultural policies with the Paris Agreement, the European Green Deal, and the United Nations Sustainable Development Agenda. For agri-food SMEs with limited financial capacity, digital technologies are therefore becoming more than a modernisation tool; they increasingly represent a strategic mechanism for reducing food waste, conserving energy, and improving resource circularity in line with SDG 12. Yet the digital transformation of the Turkish agri-food sector remains at an early stage. Existing evidence indicates that digital adoption across the sector remains fragmented, poorly coordinated, and difficult to scale beyond pilot projects, particularly in relation to precision agriculture, AI applications, and data-driven decision systems [27,28].

These structural conditions suggest that the transition towards a digitally enabled CE in developing-country agri-food SMEs is shaped by multiple interacting determinants rather than by technology adoption alone. Financial support mechanisms, investment costs, managerial learning orientation, analytical capability, food waste prevention capacity, environmental performance, regenerative infrastructure, and regulatory frameworks may jointly determine whether digitalisation can effectively support circular transformation. Nevertheless, previous studies have rarely examined these determinants as an integrated decision problem in emerging-economy contexts.

To address this gap, this study adopts the Technology–Organisation–Environment (TOE) framework [40] as the primary theoretical lens and combines it with the PESTEL perspective to identify the critical contextual dimensions influencing digitalisation in the CE transition. To prioritise these factors under uncertainty, the study employs the Spherical Fuzzy Analytic Hierarchy Process (SF-AHP). The SF-AHP is an advanced multi-criteria decision-making method that models the uncertainty in expert judgements across three independent dimensions: membership, non-membership, and hesitation. Unlike traditional fuzzy approaches, this method incorporates the level of hesitation and ambiguity directly into the mathematical process, providing a more realistic and robust prioritisation. The SF-AHP allows experts to express hesitation, uncertainty, and ambiguity simultaneously, thereby providing a more realistic evaluation framework for complex sustainability decisions involving multiple qualitative criteria. Consequently, it produces more precise and reliable results for problems characterised by ‘grey areas’, such as the strategic prioritisation of CE factors [41].

This study contributes to the literature in three important ways. First, it extends the digitalisation–CE literature by focusing on SMEs in a developing-country context, where institutional and structural conditions differ substantially from those in advanced economies. Second, it integrates the TOE framework with PESTEL analysis to provide a more context-sensitive understanding of digital transformation in circular agri-food systems. Third, by prioritising the identified success factors through SF-AHP, the study offers a practical decision-support framework that can assist policymakers and managers in designing more targeted interventions to advance sustainable production and consumption in line with SDG 12.

The remainder of the study is organised as follows. Section 2 reviews the relevant literature and identifies the critical success factors. Section 3 explains the research methodology. Section 4 presents the empirical findings. Section 5 discusses the results in relation to prior literature. Section 6 outlines the theoretical and practical implications. Finally, Section 7 presents the conclusions, limitations, and directions for future research.

2. Literature Review

The paper investigates the success factors for digitalisation in the transition to a CE for agri-food SMEs in developing countries. The PESTEL framework is used as a structuring lens to classify and organise the identified success factors. The study adopts the TOE framework as a theoretical basis to explain how these factors gain meaning under technological, organisational, and environmental dimensions. After structuring the identified factors within the PESTEL framework, their importance is evaluated using the SF-AHP method. This approach enables a holistic evaluation of the identified factors, both theoretically and methodologically, within a single analytical framework. AHP, FAHP, and SF-AHP analyses were conducted using Microsoft Excel version MS Excel 2013.

2.1. Theoretical Background

The TOE framework, developed by [40], is a comprehensive theoretical model that describes the process by which an organisation adopts and implements technological innovations. The model’s strength lies in its holistic approach to adoption decisions at the organisational level, encompassing technological, organisational, and environmental dynamics. According to the TOE framework, the innovation adoption process is shaped around three key contexts [42]:

• Technological context: This encompasses existing technologies already used by the organisation and new technologies available on the market that are relevant to the organisation’s transformation. This dimension describes characteristics such as the complexity, compatibility, and relative advantages the technology will provide to the organisation.
• Organisational context: This refers to the internal structure of the organisation. The size, scope, availability of resources (financial, human), organisational culture, and management structure of the organisation are key elements of this context.
• Environmental context: This represents the external arena in which the organisation operates. External factors such as sectoral competition, government regulations, incentives, legal infrastructure, and technology providers have a decisive influence on adoption decisions.

Omrani et al. state that the key advantage of the TOE framework in explaining the adoption of digital technology by SMEs, distinguishing it from other behavioural models, is its ability to evaluate both internal and external determinants within the same theoretical framework [43]. In their analysis of large-scale SME data, the authors revealed that organisational readiness is the strongest factor influencing digitalisation decisions. This finding indicates that the financial resources, employee competencies, and managerial support available to organisations play a central role in digital transformation decisions. Furthermore, the existing information technology infrastructure and the availability of digital tools were identified as significant facilitators in the adoption of new technologies. In contrast, environmental factors, particularly competitive pressure and external constraints, were found to have a more limited impact on digitalisation decisions. These results support the explanatory power of the TOE framework, demonstrating that digital transformation in SMEs is shaped more by the internal capacity and readiness of the organisation than by external pressures.

Digital transformation in agricultural enterprises has different dynamics from those in the manufacturing or service sectors, due to sector-specific challenges such as seasonality, long biological cycles, and dispersed production structures. In this context, the Technology-Organisation-Environment (TOE) framework offers a strong theoretical foundation for explaining the complex and multifaceted nature of agricultural transformation. A recent study on leading agricultural enterprises in China revealed that digital transformation occurs not through singular factors, but through the simultaneous interaction of technological, organisational, and environmental conditions [44]. This study demonstrates that the TOE framework transcends narrow approaches that focus solely on internal capabilities or only on external pressures. The authors emphasise that a robust digital infrastructure is a ‘fundamental support’; however, for this infrastructure to translate into high-quality development, investments in smart equipment (technological context), human capital, and internal control systems (organisational readiness) must work together in an integrated manner. Therefore, the TOE model is a particularly suitable framework for explaining the digitalisation pathways of businesses in the agri-food sector and identifying which factors complement each other.

The application of the TOE framework in agri-food SMEs offers critical operational and environmental advantages. Through this model, precision agriculture techniques enhance productivity, while end-to-end traceability across the supply chain improves transparency and optimises food safety. In addition, technologies such as blockchain enable real-time responses to potential food safety risks, while the intelligent management of resources (water, energy, and pesticides) contributes to minimising the ecological footprint of agricultural production [45].

2.2. Subject-Based Literature Review

As part of this study, a systematic literature review was conducted to identify the factors influencing SMEs’ digitalisation processes in the agri-food sector during their transition to a CE. The following query was used in the Web of Science database search: (TS = (“circular economy”)) AND (TS = (“blockchain” OR ‘AI’ OR “artificial intelligence” OR “digital*”)) AND (TS = (“driver*” OR “barrier*” OR ‘success’)) AND (TS = (“agri-food” OR “food industry”)). This search yielded 31 key articles directly related to the topic. These articles were examined in terms of their titles, abstracts, and content, and those outside the scope of the research were excluded. Subsequently, critical success factors were identified using the PESTEL framework. This section presents the existing literature, while the critical success factors identified within the PESTEL framework are discussed in Section 2.4.

The integration of CE and digital technologies stands out as a key determinant of sustainable transformation, particularly in agri-food supply chains. Within this framework, it has been demonstrated that blockchain technology has significant impacts on critical success factors such as reducing food waste, enhancing food safety, and ensuring traceability throughout the product life cycle [24]. Similarly, the integration of technologies such as blockchain, IoT, and AI supports CE applications by providing transparency, traceability, and operational efficiency [12]. In this technological transformation process, mechanisms such as improved data accuracy, reduced information asymmetry, and optimised supply chain processes are considered key elements accelerating the transition to a CE [46]. From the perspective of Industry 4.0 technologies, it has been observed that smart sensors, big data, and AI enhance resource efficiency, reduce waste, and contribute to sustainability goals [37]. Additionally, digital technologies are reported to strengthen enablers of the CE by supporting business model innovation, while AI-driven open innovation accelerates the adoption of circular supply chains [23,47].

The integration of digital technologies into life cycle assessment processes improves decision-making by enhancing data accuracy and transparency [48], while also creating significant impacts on productivity, quality, and sustainability in the agricultural sector [49]. From a broader perspective, it has been emphasised that digital transformation contributes to the development of sustainable and circular systems by transforming production processes, and that this transformation is multidimensional in nature [50,51]. Within this framework, research indicates that digital technologies support elements such as business continuity, waste reduction, and organisational learning by enhancing sustainable supply chain performance [52]. Additionally, digital transformation serves as a critical tool for overcoming barriers encountered in circular food production and consumption [53].

Studies focusing on the factors determining success in digitalisation during the transition to a CE indicate that top management support, organisational flexibility, digital infrastructure, and sustainability-focused management strategies play a critical role [37]. However, it is noted that organisational and environmental factors are particularly decisive in developing countries [10], and that in the context of SMEs, leadership, organisational culture, and management strategy have significant impacts on digital transformation performance [54]. Furthermore, firms’ digital capacity and knowledge levels are highlighted as critical to the success of CE practices [55], while business strategy and technological innovation are decisive for supply chain resilience [56]. Additionally, innovation and digitalisation are emphasised as fundamental driving forces behind sustainable food systems [57].

However, the literature also points to significant barriers to digitalisation in the transition to a CE. In particular, it is highlighted that SMEs lag in this transformation due to cost, infrastructure, and competency gaps [58]. Costs, regulatory gaps, and technological literacy are significant barriers to the adoption of digital technologies [12], while regulatory frameworks and infrastructure deficiencies in blockchain applications emerge as critical limiting factors [59]. Economic, institutional, and cultural barriers are particularly pronounced among SMEs [48], and cost and digital literacy issues constrain small producers [49]. In the transition to a CE, information gaps, policy inadequacies, and high costs are also among the major obstacles [60]. Furthermore, organisational factors constitute the most critical barriers to digital transformation towards a CE [61], and the lack of green skills in developing countries limits this transformation [62].

Finally, it is emphasised that digital transformation in the transition to a CE is not merely a technological process but also a human- and organisation-centred one. In this context, it is noted that AI and human-in-the-loop approaches play an important role in strategic decision-making processes [58], and that the skills required for digital and circular transformation are multidimensional, necessitating the simultaneous consideration of technical, social, and transformational skills [63]. In addition, large-scale firms are reported to play a critical role in improving the sustainability and digitalisation capacities of SMEs [57].

In summary, there is a strong consensus in the literature that digital technologies play a significant enabling role in the transition to a CE, particularly by enhancing sustainability performance in agri-food supply chains. However, existing studies largely focus on specific technologies (e.g., blockchain, AI, or IoT) and primarily address their impact through conceptual, qualitative, or one-dimensional analyses. Notably, studies prioritising the critical success factors enabling digitalisation during the transition to a CE are quite limited. Furthermore, a significant proportion of the findings in the literature are based on large-scale enterprises and developed country contexts, and the role played by financial, technological, and institutional constraints faced by SMEs in developing countries has not been sufficiently clarified. Consequently, there is a significant research gap regarding the systematic and comparative evaluation of critical success factors influencing digitalisation in SMEs’ transition to the CE within developing countries, particularly using decision-making approaches under uncertainty.

2.3. Method-Based Literature Review

This section focuses on the decision-making techniques used to determine the criterion weights of critical success factors in the transition of firms in the food and agriculture sector towards the CE. It outlines the methods employed in previous studies and explains the rationale for adopting SF-AHP in this research.

Previous research has used the integrated ANP-MAIRCA methodology to examine the interrelationships among success factors and to rank priority criteria for the transition to a CE in the food and agriculture sector [24]. In that study, factors such as the ability to prevent food waste, increased food safety, and product lifecycle tracking were identified as important success criteria [24]. Another study prioritised the key factors enabling the implementation of Industry 4.0 to achieve a CE in the supply chain, using the group-based fuzzy AHP method [10]. There, the desire to acquire new knowledge and knowledge of the CE were found to be high-weight factors. A notable study applied a hybrid approach—IVSF-DEMATEL-ISM-MICMAC—to identify critical success factors for integrating AI into the supply chain during the transition to a CE [23]. Policy implementation, guidance, and the development of information infrastructure emerged as key enablers. Another study, which aimed to identify critical success factors in digital transformation to overcome barriers to circular food consumption and production, employed a hybrid approach using the PqROF and CoCoSo’B methods [53]. Factors such as accurate demand forecasting, technological innovations to reduce food loss and waste, and improved decision-making accuracy received the highest scores. In research investigating barriers to the application of IoT technologies in the transition to a CE and performing criterion weighting, the SF-AHP method was utilised [62]. Here, data privacy and security, technological complexity, and high initial investment costs were identified as the most significant factors. Another study examining the contribution of digital transformation to sustainable development adopted a hybrid approach combining SF-AHP and SF-DEMATEL [64], identifying the transition to a CE for waste reduction and the development of smart communities as top priorities.

Across the reviewed literature, the SF-AHP method has been widely applied to determine the importance weights of criteria. SF-AHP is a multi-criteria decision-making method that allows decision-makers to express their judgements linguistically, accommodates uncertainty, and provides consistent quantitative outcomes [41]. The prioritisation of digitalisation factors in SME transitions towards a CE is characterised by considerable uncertainty and subjective judgement, primarily due to the limited availability of data and resources within SMEs. Consequently, expert judgement becomes critical in managing such decision-making processes. SF-AHP offers significant advantages over traditional models, especially in its effectiveness at capturing human uncertainty in expert assessments, leading to more sensitive and reliable results. Unlike conventional fuzzy logic approaches, this method enables the independent representation of membership, non-membership, and hesitation degrees within a spherical fuzzy environment, allowing for more flexible and accurate outcomes in highly uncertain decision-making contexts [41].

In this study, the SF-AHP method was adopted to identify the most important criteria. Additionally, solutions were obtained using the AHP and F-AHP methods, and the importance weights of the criteria were compared. Sensitivity analysis was also conducted under different scenarios. Through comparative solutions and sensitivity analysis, robust and reliable outcomes were achieved. As a result, this study provides a more realistic and reliable framework for identifying the strategic priorities required for SMEs to allocate their limited resources towards the most critical success criteria.

2.4. Identification of Factors Based on the PESTEL Framework

The transition to a CE for companies is a multi-layered transformation process shaped by policy regulations, economic constraints, social dynamics, technological infrastructure, environmental pressures, and legal frameworks. Digitalisation in the agri-food sector can support CE applications by interacting with these macro- and micro-environmental factors [65]. Against this backdrop, the PESTEL (Political, Economic, Social, Technological, Environmental, and Legal) framework was utilised as a preliminary classification and analytical coding structure to provide a multidimensional dissection of these factors [24]. Building on the ETPS model developed by [66], the PESTEL approach is now widely used in current studies as a tool for the comprehensive analysis of external environmental factors in digital transformation and sustainability processes [67]. Accordingly, all identified factors are presented in

Table 1. Success Factors.

Factor No.	PESTEL	TOE	Source	Factors	Description
C1	P	E	[10]	Government subsidies for enterprises to deploy I4.0 technologies	Government financial grants to facilitate the adoption and integration of Industry 4.0 technologies within enterprises.
C2	E(c)	O	[60]	High investment costs	The high initial capital required for implementing technological infrastructure, new business models, and green activities that enterprises need when transitioning to a CE model.
C3	S	O	[10]	Willingness to learn new knowledge or deploy I4.0 technologies	The motivation and openness of enterprise management and employees to acquire the new knowledge and skills necessary for modernising traditional business models and integrating Industry 4.0 solutions into operations.
C4	T	T	[37]	Data analytics to boost agricultural output	The use of analytical tools and techniques to analyse historical data, providing precise forecasts for production, climate, and resource needs to enhance agricultural productivity and profitability.
C5	T	T	[53]	Efficient technology for the CE process	Technological innovation with supply chain partners to optimise circular portfolios through strategic data governance.
C6	E(N)	T	[24]	Ability to prevent food waste	Integrating smart contracts and modernised logistics to reduce waste and waiting times through automated inventory control, real-time order processing, and systematic recycling tracking.
C7	E(N)	O	[10]	Energy conservation and environmental protection	Transforming operational processes into a sustainable structure by reducing work intensity and resource consumption through energy efficiency, waste management, and resource recycling.
C8	E(N)	T	[56]	Regenerative agriculture infrastructure	Regenerative agriculture infrastructure is a technological innovation factor that supports long-term sustainability and resilience in food supply chains.
C9	L	E	[12]	Regulatory uncertainty and lack of a standardised metric and measurement method for a CE	The lack of legal infrastructure and standards hinders the integration of new technologies. The absence of universally accepted performance indicators and evaluation frameworks effectively impedes the monitoring and reporting of progress in CE initiatives

below.

Türkiye, while offering a strong foundation for digital agriculture applications due to its considerable agroecological diversity and production potential, faces significant structural constraints in the diffusion of digital transformation.

• Government subsidies for enterprises to deploy Industry 4.0 technologies: The generally limited budgets and scarce resources of agri-food SMEs make the adoption of digital technologies challenging. A successful transition to the CE largely depends on the synergy between economic feasibility and market activation, which in turn requires a regulatory and supportive role from governments. Governments should create appropriate political, economic, and legal environments, and provide subsidies to enterprises to facilitate the adoption of Industry 4.0 technologies [68]. In Türkiye, especially for SMEs, initial capital requirements, subscription fees, and technological infrastructure costs hinder the uptake of digital solutions. In this context, state subsidies emerge as a key financial lever facilitating the adoption of digital agricultural technologies [28,39].
• High investment costs: Due to the small-scale and fragmented nature of enterprises, high investment costs for accessing digital equipment, sensor systems, and smart agricultural technologies constitute a significant limiting factor. For SMEs in particular, the burden of initial capital, subscription fees, and infrastructure costs slows the adoption of digital solutions [28,39,60].
• Willingness to learn new knowledge or deploy Industry 4.0 technologies: As Industry 4.0-based agricultural technologies are rapidly evolving, the willingness of practitioners in the agri-food sector to learn and apply these technologies is crucial. Motivation at the individual level enables employees to adapt to the digital transformation process and to use new technological tools effectively. However, studies emphasise that farmers’ limited knowledge base, age, and attachment to traditional practices can impede knowledge sharing and technological adaptation [68]. In rural areas of Türkiye, the high average age of farmers, limited digital skills, inadequate digital competence among advisory personnel, and the cultural distance between AgTech providers and users make it difficult for farmers to adopt digital agricultural technologies and weaken their willingness to embrace new knowledge and Industry 4.0 applications [28].
• Data analytics to boost agricultural output: The application of big data analytics and AI enables real-time decision-making, optimises resource use, and enhances the productivity of agri-food supply chains [37]. In Türkiye, the lack of systematic data collection from production sites and limited integration across platforms significantly restrict the use of data analytics to increase agricultural output. In particular, the limited adoption of International Standardization Organization Binary Unit System (ISOBUS ISO 11783 [69])-based data collection systems outside large-scale enterprises curtails the effective use of decision support systems [27].
• Efficient technology for the CE process: Efficient use of technology in the CE process refers to technological innovation undertaken collaboratively with supply chain partners. Robust technological infrastructures and data analytics reduce uncertainty in circular supply chain applications and improve decision-making accuracy. This enables enterprises to ensure environmental sustainability while also reducing operational costs and gaining a competitive advantage [23].
• Ability to prevent food waste: Preventing food waste relies on technological capabilities such as shortening transaction times through smart contracts and minimising errors via automated inventory management. These systems support the core objectives of the CE by extending the lifecycle of food products and enabling systematic recycling tracking [24]. In Türkiye, food loss and waste represent key policy challenges, particularly concerning the United Nations Sustainable Development Goal (SDG) 12.3, which aims to halve global food waste by 2030. Food loss in Türkiye aligns with global averages but reaches millions of tonnes, primarily owing to technological infrastructure deficiencies and coordination gaps during harvesting, logistics, and storage [70]. Digital traceability is the core operational capability that enhances resource efficiency in circular agricultural systems. The ability to prevent food waste encompasses the integration of modern logistics processes and smart contracts to minimise delays, the management of product flows through automated inventory control and real-time order processing, and the establishment of systematic recycling tracking across the supply chain, from harvest to consumption [24]. Achieving SDG 12 targets requires not only awareness but also the effective operational use of data-driven decision-making and intelligent systems, which are crucial for systematically reducing food loss by improving coordination across the supply chain [70].
• Energy conservation and environmental protection: The integration of Industry 4.0 technologies into agri-food applications enhances efficiency, minimises waste, and maximises environmental protection. This energy-saving, environmentally focused approach is a key enabler of the CE [10]. In Türkiye, the limited diffusion of digital solutions in agricultural production constrains the optimisation of resource use and hinders the full realisation of energy-saving and environmental protection, primarily because precision management of water, fertiliser, and energy remains limited [28].
• Regenerative agriculture infrastructure: In circular agri-food systems, digital transformation increasingly relies not only on data-driven technologies but also on production infrastructures that restore ecological resources while maintaining operational efficiency [56]. Studies have shown that regenerative agricultural systems supported by digital technologies can improve soil health, reduce input dependency, and enhance long-term supply chain resilience [61,71]. Economically, this integration lowers costs, facilitates market access, and creates new income opportunities; socially, it improves farmers’ access to information, strengthening quality of life and rural resilience [72]. In Türkiye, challenges such as soil degradation, water scarcity, fragmented farm structures, and intensive chemical input use make regenerative production infrastructure particularly critical for SMEs aiming to implement CE practices [73].
• Regulatory uncertainty and lack of standardised metrics for a CE: In developing countries, the absence of a coherent regulatory framework directly impedes the implementation of digital technologies in the agri-food sector [12]. In Türkiye, another structural barrier to circular economy-oriented digital transformation is the lack of standardised performance indicators and an institutional regulatory framework for monitoring circular practices. Existing studies indicate that there is no common assessment system for measuring circularity performance at the firm level in Türkiye, making it difficult for firms to compare the environmental outcomes of their digital investments [60]. Thus, regulatory uncertainty and the lack of measurement standards are significant environmental factors that complicate the monitoring and reporting of CE practices, particularly for agri-food SMEs.

3. Research Methodology

The research methodology of this study adopts a multi-stage approach, designed to identify and prioritise success factors relating to digitalisation in the transition of agri-food SMEs to a CE. First, in the literature review, critical success factors were identified through a PESTEL analysis of existing studies. Each factor was subsequently mapped onto the TOE framework to reflect its technological, organisational, or environmental relevance at the firm level. The methodological process followed throughout the study is summarised step by step below. The specific stages of the applied Multi-Criteria Decision Making (MCDM) method are illustrated in Figure 1.

3.1. Fuzzy Logic and Fuzzy Set Theory

Fuzzy logic was first introduced to the literature by Zadeh [74]. Zadeh argued that fuzzy logic should be employed in processes that closely resemble human decision-making and perception, particularly in complex situations encountered in daily life where information is often uncertain. Fuzzy logic enables processes to operate in a way that is comprehensible to humans and can be analysed to reach meaningful conclusions. In a fuzzy set, real numbers on the horizontal axis are assigned membership degrees ranging from 0 to 1 on the vertical axis. In this context, it will be understood that the real number on the horizontal axis is $0\le {\mathsf{\mu }}_{\tilde{\mathrm{A}}}\left(x\right)\le 1$. Therefore, fuzzy set elements will be defined by membership functions that vary between 0 and 1. Here, the membership function is represented as $x\in \mathrm{A} \mathrm{and} \mathrm{A}\subseteq \mathrm{X} \mathrm{and} {\mu }_{\tilde{A}}\left(x\right):\mathrm{X}\to [0,1]$, indicating the degree to which (x) belongs to the set. The membership function, denoted as $\mu \left(x\right)$, takes values in the interval [0, 1]. The membership function for non-members is 0, while for full members it is 1. In cases where it is unclear whether an element belongs to a set, the membership function takes a value between 0 and 1.

3.2. Spherical Fuzzy Sets

Although there are an infinite number of fuzzy subsets, special types of fuzzy numbers have been developed to simplify the extensive mathematical processing required for fuzzy set operations. SF-AHP sets have been developed as an extension of Pythagorean, intuitionistic, and neutrosophic fuzzy sets [41]. Within the concept of spherical fuzzy sets, experts determine three independent degrees for each element: the membership degree, the non-membership degree, and the hesitancy (or uncertainty) degree. The squares of these degrees are then summed, such that their total is less than or equal to 1. Additionally, the concept of global distance is used in SF-AHP sets. Formally, the universal set (X) is defined as the spherical fuzzy set ${\tilde{A}}_s$ and is expressed as follows (1, 2):

$${\tilde{A}}_s=\{⟨\mathrm{x},{\mathsf{\mu }}_{{\tilde{A}}_s}\left(\mathrm{x}\right),{\mathsf{\nu }}_{{\tilde{A}}_s}(\mathrm{x}),{\mathsf{\pi }}_{{\tilde{A}}_s}(\mathrm{x})⟩|\mathrm{x}\in \mathrm{X}\}$$

(1)

In this notation, ${\mathsf{\mu }}_{{\tilde{A}}_s}\left(\mathrm{x}\right)\in \mathrm{X}$ represents the membership degree of the element, ${\nu }_{{\tilde{A}}_s}\left(\mathrm{x}\right)\in \mathrm{X}$ denotes the non-membership degree of the element, and ${\pi }_{{\tilde{A}}_s}\left(\mathrm{x}\right)\in \mathrm{X}$ indicates the degree of hesitancy (or uncertainty) of the decision-maker. The key feature that distinguishes spherical fuzzy sets from intuitionistic and Pythagorean fuzzy sets is the boundary condition specified in Equation (2).

$${\mu }_{{\tilde{A}}_s}:\mathrm{X}\to [0,1],{\nu }_{{\tilde{A}}_s}:\mathrm{X}\to [0,1],{\pi }_{{\tilde{A}}_s}:\mathrm{X}\to [0,1] \mathit{and} 0\le {\mu }_{{\tilde{A}}_s}^2+{\nu }_{{\tilde{A}}_s}^2+{\pi }_{{\tilde{A}}_s}^2\le 1$$

(2)

This boundary condition requires that the sum of the squares of the parameters, rather than their direct sum, must be less than or equal to 1. By expanding the decision-maker’s domain of preference to the volume of a unit sphere, this structure enables more flexible and precise modelling, particularly in complex decision-making problems characterised by high uncertainty.

3.3. Spherical Fuzzy AHP

MCDM has long been utilised to address decision problems and support choices. The Analytic Hierarchy Process (AHP), proposed by Saaty, is the most widely used decision-making method in the literature and is based on pairwise comparisons [75]. In this approach, the decision-maker arrives at a final ranking by comparing the selection criteria and alternatives. An advantage of this method is that it highlights inconsistencies during the decision stages, thereby enabling more consistent comparisons. In the classical AHP method, comparison matrices are represented by exact numbers. However, linguistic ambiguity is overlooked when using precise numerical values, which constitutes a limitation. To address this, fuzzy logic has been integrated into the AHP method [76]. As mentioned above, spherical fuzzy (SF) sets are based on the independent definition of the decision-maker’s uncertainty, as well as membership and non-membership degrees. In this context, the membership function of decision-makers is defined on a global surface [41]. Parameters are thus assigned independently across a wide range, allowing for the generalisation of other extensions of fuzzy sets. Consequently, due to these advantages, the SF-AHP method was used to assign weights to the criteria defined within the scope of this study. The research methodology comprises the following phases:

• Step 1: A literature review conducted via the Web of Science database initially identified 31 studies. Following a review, 4 articles were excluded as they fell outside the scope of the topic, and the analysis continued with 27 articles. Based on these studies, the digitalisation success factors supporting the transition process of agri-food SMEs to the CE were identified within the PESTEL framework. In consideration of the complexity inherent in pairwise comparisons within the SF-AHP method, the number of factors was restricted to nine in order to preserve the consistency of expert judgements. This decision is theoretically underpinned by Miller’s (1956) principle, which posits that the upper limit of human cognitive capacity is 7 ± 2 [77].
• Step 2: The panel consisted of three industry practitioners from the agri-food sector and two academics specialising in digitalisation and sustainability-related research. Reflecting previous expert-based MCDM research, a panel of five domain experts was adopted in this study. Prior research emphasises that, in studies seeking expertise-based and experience-driven insights, the level of expertise and depth of knowledge among participants are more important than the sample size [78]. Although there is no consensus on an exact number of experts, earlier studies suggest that between three and eight qualified experts are sufficient to generate reliable and meaningful group judgements [79]. Consistent with the previous literature, this study evaluates the opinions obtained from a five-expert panel [78,80,81]. This panel composition ensured both practical and theoretical perspectives in the evaluation process. The demographic profiles of the experts are presented in

  Table 2. Participant demographic information.

  	Profile	No. of Respondents
  Work field	Academic	2
  	Agri-food company	3
  Work position	Professor Marketing	1
  	Professor of Business Management	1
  	Company owner	1
  	Executive Manager	2
  Work Experience	>15 years	5
  Education Level	Bachelor’s degree	3
  	PhD Degree	2

  .

• Step 3: Pairwise comparison matrices are constructed using linguistic importance criteria. At this stage, experts are consulted to compare the criteria based on linguistic terms of importance. The linguistic scale employed for pairwise comparisons in the SF-AHP questionnaire is presented in

  Table 3. SF-AHP Scale.

  		Score Index	μ	ν	π
  Absolutely more importance	AMI	9	0.90	0.10	0.00
  		8	0.85	0.15	0.05
  Very high importance	VHI	7	0.80	0.20	0.10
  		6	0.75	0.25	0.15
  High importance	HI	5	0.70	0.30	0.20
  		4	0.65	0.35	0.25
  Slightly more importance	SMI	3	0.60	0.40	0.30
  		2	0.55	0.45	0.35
  Equally importance	EI	1	0.50	0.40	0.40
  		0.500	0.45	0.55	0.35
  Slightly low importance	SLI	0.333	0.40	0.60	0.30
  		0.250	0.35	0.65	0.25
  Low importance	LI	0.200	0.30	0.70	0.20
  		0.167	0.25	0.75	0.15
  Very low importance	VLI	0.143	0.20	0.80	0.10
  		0.125	0.15	0.85	0.05
  Absolutely low importance	ALI	0.111	0.10	0.90	0.00

  [41].

• Step 4: The score index (SI) is calculated using Equation (3) to obtain the consistency ratio for the pairwise comparison matrix.

$$SI=\sqrt{|100\ast [{({\mu }_{{\tilde{A}}_s}-{\pi }_{{\tilde{A}}_s})}^2-{({\nu }_{{\tilde{A}}_s}-{\pi }_{{\tilde{A}}_s})}^2\left]\right|}$$

(3)

In the second stage of Step 4, the inverse of the matrix is obtained using Equation (4).

$${\displaystyle \frac{1}{SI }}={\displaystyle \frac{1}{\sqrt{|100\ast [{({\mu }_{{\tilde{A}}_s}-{\pi }_{{\tilde{A}}_s})}^2-{({\nu }_{{\tilde{A}}_s}-{\pi }_{{\tilde{A}}_s})}^2\left]\right|}}}$$

(4)

• Step 5: The consistency index (CI) is calculated using Equation (5).

$$CI={\displaystyle \frac{{\lambda }_{max}-\mathfrak{n}}{\mathfrak{n}-1}}$$

(5)

The consistency of each pairwise comparison matrix is checked. The consistency ratio (CR) must be below 10%, as defined in Equation (6):

$$CR={\displaystyle \frac{CI}{RI}}$$

(6)

• Step 6: Spherical fuzzy local weights are calculated using Equation (7):

$$SWAM=\left\{{\left[1-{\displaystyle \prod _{i=1}^n}(1-{\mu }_{{\tilde{A}}_{si} }^2{)}^{w_i}\right]}^{1/2},{\displaystyle \prod _{i=1}^n}{\upsilon }_{{\tilde{A}}_{si}}^{w_i},\left[{\displaystyle \prod _{i=1}^n}(1-{\mu }_{{\tilde{A}}_{si} }^2{)}^{w_i}\right.-{\left.{\displaystyle \prod _{i=1}^n}(1-{\mu }_{{\tilde{A}}_{si} }^2{-{\pi }_{{\tilde{A}}_{si} }^2)}^{w_i}\right]}^{1/2} \right\}$$

(7)

• Step 7: After the local weights have been calculated, the fuzzy weights are stabilised using the stabilisation Equation (8):

$$S(w_j)=\sqrt{\left|100\ast \left[(3{\mu }_{{\tilde{A}}_s}-{\displaystyle \frac{{\pi }_{{\tilde{A}}_s}}{2}}{)}^2-({\displaystyle \frac{{\nu }_{{\tilde{A}}_s}}{2}}-{\pi }_{{\tilde{A}}_s}{)}^2\right]\right|}$$

(8)

• Step 8: The local weights are then normalised to obtain the final criterion weights, as shown in Equation (9):

$$w_j={\displaystyle \frac{S\left(\widetilde{w_j}\right)}{{\sum }_{k=1}^{n}S\left(\widetilde{w_k}\right)}},\hspace{1em}j=1,\dots ,n.$$

(9)

• Step 9: The criteria are ranked according to their importance.
• Step 10: Comparative and correlation analyses are carried out.
• Step 11: Results analysis and discussion are presented.

4. Results

The importance weights of the nine criteria were calculated using the SF-AHP method, based on survey responses from five experts. In the initial stage of the procedure, Expert 1 was asked to compare the criteria, resulting in a comparison matrix comprising real numbers. To avoid excessive table complexity, the analysis proceeded using only Expert 1’s results; the remaining opinion data are provided in Supplementary Materials.

Subsequently, the matrix was fuzzified by assigning fuzzy numbers corresponding to linguistic terms on the SF-AHP scale [41]. Local weights were calculated using the SWAM formulae described in the methods section. These local weights were then defuzzified, and the resulting values were normalised to determine the final criteria weights. Figure 2, which illustrates the criteria weights obtained for each expert, is presented below.

When the results derived from the experts’ data are examined, it is evident that the importance levels of the criteria differ to some extent (Figure 2). Nevertheless, the overall trends are broadly consistent. Notably, criterion C4 received the highest weight from all four experts, indicating that C4 (data analytics to boost agricultural output) is the most important critical success factor. In the expert evaluations, the subsequent criteria in terms of importance are C2, C3, C5, C6, and C7. Although some degree of heterogeneity in decision-making is observed among the experts, these criteria remain significant determinants, as the weight scale does not display a particularly wide distribution. Furthermore, these criteria can be considered as supporting factors for the primary success criterion. In contrast, criteria C1, C8, and C9 have received relatively low weights in the figure. However, these criteria should not be entirely disregarded; rather, they should be evaluated as supportive elements within a holistic framework. The combined results are presented in Figure 3 below.

When examining the criterion weights derived from the combined expert opinions, it is evident that the weights are distributed across a very narrow range, similar to the previous table. This distribution suggests that the criteria are not mutually exclusive but instead form a cohesive and supportive structure. Consequently, it highlights the need for policymakers and firm managers to adopt a holistic perspective in their evaluations. Notably, criterion C4 (data analytics to boost agricultural output) emerges as the most important criterion in the results. The subsequent criteria—C2, C5, and C3—play a decisive role in supporting C4. The complex and interdependent nature of key success factors in the transition to a CE facilitates the interpretation of these findings. In particular, the close weighting of criteria C7, C8, and C6 indicates that these are complementary factors and should be evaluated collectively. Finally, although criteria C9 (regulatory uncertainty and lack of a standardised metric and measurement method for a CE) and C1 (government subsidies for enterprises to deploy Industry 4.0 technologies) received lower weights, the overall narrow distribution of weights suggests that they should not be disregarded. This demonstrates that each criterion in the decision model holds complementary importance. Although the model derived from the integration of expert opinions presents a balanced and methodologically consistent priority structure, the results indicate that particular attention should be given to criteria C4 (data analytics to boost agricultural output) and C2 (high investment costs) by decision-makers.

4.1. Comparative Analysis

The problem addressed in this study is to determine the importance weights of the success factors for digitalisation in the transition process of SMEs in the agri-food sector towards a CE. Accordingly, the criteria identified from the literature were analysed using the global fuzzy AHP method, enabling firms and policymakers to take appropriate actions and make informed strategic decisions regarding these issues. Given that generalising results obtained from a single method lacks a robust foundation, the importance of solving the same problem using different AHP approaches and conducting a detailed comparison of the results becomes clear. Consequently, it was concluded that applying multiple AHP methods would be advantageous. In this context, this section aims to address the problem using two different versions of AHP (AHP and Fuzzy AHP). The results of AHP and F-AHP are presented in Figure 4 and Figure 5, respectively.

Figure 6 presents the comparative importance weights obtained from the SF-AHP, F-AHP, and AHP methodologies.

Upon examining the total weights, it is evident that the three methods produced consistent results. Furthermore, as shown in

Table 4. Correlation Analysis Results.

	SF-AHP	F-AHP	AHP
SF-AHP	1	0.986	0.910
F-AHP		1	0.986
AHP			1

, the correlation coefficients derived from the analysis indicate a high degree of correlation between each method. This demonstrates that the comparisons are based on a strong consensus.

Upon examining the results obtained from all methods, it is evident that criterion C4 (data analytics to boost agricultural output) has the highest importance score. The consistent identification of C4 as the most significant criterion across all methods demonstrates that it is the most critical success factor in the context of this decision problem, thereby supporting the robustness of the results obtained from different approaches. The next most influential criteria are C2, C3, and C5, indicating that these are fundamental determinants within the decision problem and play a supportive role for the key success factor. It is also apparent that criteria C6, C7, and C8 do not exhibit significant differences across the methods; thus, these can be considered as supporting criteria within the decision model. Finally, the criteria with the lowest importance weights are C1 (government subsidies for enterprises to deploy Industry 4.0 technologies) and C9 (regulatory uncertainty and lack of a standardised metric and measurement method for a CE). Although these criteria have relatively low importance weights, the mutually supportive and interconnected nature of each success factor in the transition to a CE means that all factors should be evaluated holistically. Therefore, even if assigned the lowest weights, they must not be overlooked during evaluation. The final ranking results are presented in

Table 5. Final Ranking Results.

	SF-AHP		F-AHP		AHP
C4	0.128	C4	0.177	C4	0.173
C2	0.123	C2	0.157	C2	0.159
C5	0.114	C5	0.131	C3	0.145
C3	0.111	C3	0.115	C5	0.110
C7	0.111	C7	0.101	C6	0.096
C8	0.102	C6	0.098	C7	0.093
C6	0.108	C8	0.094	C8	0.090
C9	0.102	C9	0.068	C9	0.070
C1	0.093	C1	0.058	C1	0.063

.

Following the comparative analysis, it is evident that the results obtained from the F-AHP and AHP methods are closely aligned. This similarity does not present a contradiction with the SF-AHP method; rather, it reinforces its outcomes. The theoretical underpinning of the SF-AHP approach, which incorporates uncertainty, is substantiated by the concordant results from the other analyses, demonstrating its capacity to yield more balanced outcomes and facilitate flexible modelling. Notably, the overall evaluation reveals that all three methods generate analogous rankings, thereby confirming the model’s methodological consistency. Such consistency indicates that the criterion weights derived from the analyses may serve as a robust and reliable reference point. Within the findings, criterion C4 (data analytics to boost agricultural output) emerges as the most significant. Nevertheless, criteria C2, C3, and C5 are also identified as critical determinants for the success of digitalisation in the transition of agri-food SMEs towards a CE. It is therefore imperative that policymakers and practitioners prioritise these criteria. Furthermore, the analysis underscores that these criteria should not be considered in isolation, but rather within a holistic framework, in order to ensure successful outcomes.

4.2. Sensitivity Analysis

To assess the robustness of the criterion weights derived from the SF-AHP method, a leave-one-out (LOO) sensitivity analysis was performed. The central premise of LOO analysis is to determine whether the results of a model change significantly when a key unit or condition is systematically excluded. If no substantial variation is observed across scenarios, the findings of the proposed research method may be considered robust [82,83]. In line with this approach, the SF-AHP calculations were repeated five times, each time excluding the judgement of one expert and using the evaluations of the remaining four experts. This process generated five alternative scenarios in total. The criterion rankings from each scenario were subsequently compared with the original ranking, which included all experts, by employing Spearman’s rank correlation coefficient.

The results of the LOO analysis are presented in

Table 6. LOO Analysis Results.

Scenarios	Sen-1	Sen-2	Sen-3	Sen-4	Sen-5	All_Expert
sen-1	1.000
sen-2	0.683	1.000
sen-3	0.483	0.883	1.000
sen-4	0.833	0.767	0.700	1.000
sen-5	0.717	0.617	0.600	0.933	1.000
all_expert	0.917	0.817	0.733	0.967	0.867	1.000

. In four out of five scenarios, the Spearman rank correlation coefficient exceeded 0.80, indicating that the criterion rankings remain largely unchanged even when any single expert is excluded from the analysis. These findings demonstrate that the criterion weights derived from the SF-AHP method are robust across the five different scenario compositions in which one expert is omitted.

5. Discussion

In this study, the critical success factors associated with the digitalisation of SMEs operating in the agri-food sector during their transition to a CE were identified within the PESTEL framework. Subsequently, these factors were analysed using the SF-AHP method, and their importance weights were determined. The findings indicate that the digitalisation of SMEs in the agri-food sector, in the context of the transition to a CE, constitutes not merely a technological transformation but a multi-layered process encompassing economic, organisational, and environmental dimensions. While the PESTEL framework facilitated the classification of these factors, the TOE perspective elucidates whether their influence primarily arises from technological capability, organisational readiness, or environmental pressure.

In particular, data analytics to boost agricultural output (C4) emerged as the most critical success factor across all methods. This result is consistent with studies demonstrating that digital technologies such as big data analytics, AI, and IoT enhance efficiency, resource effectiveness, and decision-making accuracy in agri-food systems [37,49,53]. Varbanova et al. (2025) emphasise that data analytics and management information systems (MIS) play a pivotal role in enhancing productivity and sustainability in modern agri-food enterprises [37]. Big data analytics and AI applications enable real-time decision-making processes that optimise resource use and improve the overall efficiency of the agri-food supply chain. Moreover, the literature indicates that automation and real-time analytics strengthen supply chain resilience, reduce waste, and improve resource efficiency in alignment with CE principles [12,37]. These technologies support both economic and environmental sustainability by improving production forecasts, optimising resource use, and strengthening supply chain coordination. However, Khan et al. (2023) identify twelve key barriers limiting the adoption of IoT applications in food supply chains and emphasise that overcoming these barriers is essential for a sustainable transformation [84]. These barriers include complex system architecture, high investment and operational costs, insufficient IT infrastructure, lack of regulatory standards, low awareness, shortage of skilled personnel, trust and data-sharing issues, lack of information management systems, data heterogeneity, supplier limitations, as well as cybersecurity and privacy risks. In developing countries, infrastructure deficiencies and financial constraints further impede the diffusion of IoT technologies. Consequently, the effective implementation of IoT is widely emphasised in the literature as requiring public support, infrastructure investment, education and awareness-raising initiatives, as well as robust collaboration among stakeholders. A study conducted in the Turkish context demonstrates that IoT/IoE-based smart agriculture technologies yield significant benefits, including increased productivity, optimisation of resource use, cost reduction, product traceability, disease detection, and support for sustainable production. However, multidimensional barriers continue to restrict their widespread adoption. In particular, low digital literacy, a lack of technical skills, and insufficient training opportunities hinder users’ ability to operate these systems effectively, while low awareness and uncertainty regarding perceived benefits negatively influence technology acceptance. Furthermore, system complexity and poor user-friendliness deter adoption, whereas high costs and inadequate infrastructure (internet and IT systems) constrain implementation. Data security and privacy risks, alongside threats of cyberattacks and data manipulation, engender a lack of trust among stakeholders and reluctance to share information [30]. Taken together, these findings suggest that the successful implementation of IoE in the agri-food sector requires not only technological advancements but also the concurrent development of socio-technical dimensions, such as human factors, education, trust, and infrastructure. From an applied perspective, this indicates that managers should regard investments in data infrastructure and analytical capabilities not merely as operational tools, but as strategic levers for circular transformation. Policymakers, meanwhile, should develop digital infrastructure support and incentive mechanisms for data-driven technologies, particularly to facilitate SME access.

High investment costs (C2), which rank second, constitute a fundamental barrier frequently emphasised in the literature. Financial constraints, infrastructure deficiencies, and substantial start-up costs are noted as significant obstacles to the adoption of Industry 4.0 and CE applications among SMEs in the agri-food sector [12,58,60,85]. This situation underscores that the circular transition represents not only a technological process, but also an economic and structural transformation [60]. Accordingly, the development of financial support mechanisms—such as grants, tax incentives, and public–private partnerships—is critical. Without such measures, even technologies with substantial potential benefits are unlikely to achieve widespread adoption, particularly in developing countries.

According to the results of our study, efficient technology for the CE process (C5) ranks third among all success factors and is identified as a critical priority. This finding is directly aligned with the literature, which emphasises that digitalisation in agri-food supply chains is not merely a supporting function but constitutes a foundational infrastructure enabling circularity [12]. Yontar demonstrates that efficient technologies such as blockchain enhance trust and collaboration among stakeholders by providing a “single version of truth” regarding resource utilisation and waste management. According to the author, this technological efficiency improves traceability throughout the product life cycle and enables the digital optimisation of waste management and recycling processes [24]. Conversely, Chen et al. conceptualise this process from the perspective of “strategic data governance,” highlighting that AI-driven open innovation breaks down data isolation between supply chain partners [23]. The authors argue that such technological integration enables organisations to collaborate with external partners and to address complex sustainability challenges by optimising circular product portfolios. Consequently, the high importance of factor C5 confirms the need for both a transparent traceability infrastructure and a partnership-based data management model for agri-food SMEs in Türkiye as they transition towards a circular economy.

The high ranking of “willingness to learn new knowledge or deploy Industry 4.0 technologies” (C3) is consistent with the literature, which emphasises that the success of CE practices depends not only on access to technological capabilities and data utilisation, but also on organisational learning, employee skills, and openness to innovation [10,51,61,63]. This factor reflects the motivation and openness of managers and employees in agri-food enterprises to acquire the new knowledge and skills necessary to modernise traditional business models and integrate Industry 4.0 solutions into operations. Technological transformation is not merely a matter of technical infrastructure, but also a process of individual motivation and learning. At the individual level, this willingness constitutes a key driving force enabling employees to adapt to the digital transformation process and to use next-generation technological tools effectively [10]. Integrating collaborative work into a strategic data management model can significantly accelerate technological development by fostering an environment in which knowledge sharing and open innovation are prioritised [23]. In particular, within digital transformation processes, an adaptive organisational culture plays a critical role alongside data-driven capabilities and collaborative innovation practices. Existing studies indicate that digital skills and openness to learning are important determinants of CE and digital transformation processes [62]. In this context, agri-food SMEs should establish organisational structures that focus on continuous training, skill development, and learning. Nevertheless, field realities indicate that there are significant barriers to this theoretical willingness. The literature highlights that farmers’ limited knowledge base, age-related factors, and attachment to traditional practices impede knowledge sharing and adaptation to new technologies [68].

The literature emphasises that contextual factors such as organisational structure, infrastructure, and the regulatory environment significantly influence digital transformation and CE practices. Culture also plays a determining role in this process. Although culture does not operate directly through technology, it influences the transition to a CE via factors such as decision-making, trust, risk perception, and business practices. It has been shown that cultural factors—particularly at the SME level—shape technology acceptance, risk perception, and collaboration tendencies, thereby affecting the diffusion of CE practices [86]. Findings from the World Values Survey for Türkiye indicate that interpersonal trust is primarily based on personal relationships rather than institutional structures, collectivist tendencies are relatively strong, and levels of uncertainty avoidance are high [87]. These socio-cultural characteristics reinforce a cautious approach towards digital technologies, thereby influencing digitalisation processes in agri-food SMEs. Specifically, in rural regions of Türkiye, the high average age of farmers, limited levels of digital skills, restricted digital competence among agricultural advisory personnel, and the cultural distance between AgTech providers and users make it more difficult for farmers to adopt new technologies and diminish their willingness to learn [28]. Moreover, the prevalence of family-owned business structures, hierarchical management approaches, cautious attitudes towards change, and traditional decision-making tendencies may also affect the rate of adoption of digital technologies in agri-food SMEs in Türkiye. These cultural conditions suggest that not only financial or technological resources, but also managerial mindsets and organisational behaviour patterns, are decisive for the success of digital transformation. In this respect, the results should be interpreted within Türkiye’s specific socio-cultural and institutional context, bearing in mind that the digitalisation process cannot be considered independently of these local dynamics.

The “Energy conservation and environmental protection” (C7) factor represents one of the core operational and environmental objectives of Industry 4.0 technologies within agri-food supply chains. As highlighted by Zhao et al., this factor entails transforming operational processes into a sustainable structure that reduces work intensity and resource consumption through energy efficiency, waste management, and resource recycling [68]. A key motivation for the adoption of Industry 4.0 technologies is the direct objective of reducing energy consumption; this acts as a strategic enabler that simultaneously delivers cost reductions for enterprises and enhances environmental protection [10]. The emphasis on “reducing work intensity” is particularly significant, as it reflects the elimination of unnecessary processes and energy-intensive repetitive tasks through technological automation, thereby improving overall system efficiency. These findings are strongly aligned with results presented specifically for the Turkish agri-food sector. Yontar (2023) [24] identifies that the main contributions of digitalisation to the CE are the enhancement of resource efficiency and carbon footprint tracking. In Yontar’s model, the optimisation of energy and raw material use in agri-food networks through digital tools plays a fundamental role in reducing operational costs. The study also demonstrates that carbon footprint tracking constitutes a necessary technological infrastructure for measuring emissions arising from energy use and for preventing waste. However, field realities in Türkiye make it difficult to fully realise the potential of energy efficiency and environmental protection. As identified by Höllinger and Sener (2025), the limited diffusion of digital solutions in rural regions of Türkiye constrains the capacity for precision management in the use of water, fertiliser, and energy [28]. These technological limitations and digital skill gaps hinder the optimisation of resource use, thereby preventing the full realisation of energy-saving and environmental protection potential. An analysis of agricultural cooperatives in Türkiye further reveals that structural issues, such as deficiencies in energy and waste management and inadequate environmental management systems, restrict circularity [60]. The study emphasises that, owing to the high implementation costs of green practices and ineffective recycling policies, waste cannot be recovered as an energy source. This indicates that, in order to achieve the C7 factor in agri-food SMEs in Türkiye, not only technological transformation but also managerial deficiencies and high-cost perceptions must be addressed. Consequently, the C7 factor should be regarded as an essential strategic priority for agri-food SMEs in Türkiye, both to control energy costs and to achieve competitive advantage through alignment with global sustainability standards.

Recent trends in the literature indicate that the success of regenerative agriculture is increasingly dependent on the integration of digital technologies into agricultural production systems. In particular, technologies such as IoT, artificial intelligence, blockchain, and nanosensors are reported to enhance environmental monitoring capacity, making regenerative agriculture practices more resilient and sustainable, while also facilitating the adaptation of small-scale producers to these practices [61,72]. From a TOE perspective, successful digitalisation in circular agri-food SMEs depends on the alignment between technological capabilities and environmental sustainability requirements. In this context, the “regenerative agriculture infrastructure” (C8) factor represents the infrastructural foundation through which digital technologies can support resource recovery, ecosystem restoration, and resilient production systems [56]. However, the relatively low ranking of this factor suggests that experts perceive regenerative agriculture infrastructure not as a short-term driver of digitalisation, but rather as a long-term transformation condition. Similarly, although it is emphasised that the integration of digital agriculture with regenerative production systems can enhance soil health, resource efficiency, and climate resilience, these benefits are largely realised only when sufficient organisational readiness and technological capacity are in place [71]. In the Turkish context, regenerative agriculture is regarded as an important approach capable of strengthening agricultural sustainability in the face of increasing soil degradation and ecological pressures [73]. Accordingly, the present finding suggests that agri-food SMEs in Türkiye recognise the strategic importance of regenerative infrastructure; however, due to limited resources, they tend to prioritise more immediate digitalisation concerns such as investment costs and operational efficiency in the short term.

In our analysis, the “ability to prevent food waste” (C6), which ranks seventh among nine factors, confirms that preventing food waste is not an isolated endeavour; rather, it constitutes an operational capability that can only be realised with the maturation of foundational competencies such as data analytics (C4) and technological infrastructure (C5), both of which rank higher. Yontar (2023) [24] argues that the integration of smart contracts and modernised logistics processes reduces logistics delays and processing times, which are among the principal structural challenges in the agri-food chain. According to the author, blockchain-based automated inventory control and real-time order processing mechanisms ensure that food products reach consumers without spoilage, thereby directly preventing waste resulting from waiting times. Furthermore, the study highlights that establishing systematic recycling tracking extends the resource lifecycle and technically enables waste minimisation, a core principle of the circular economy. In the Turkish context, given the chronic deficiencies in post-harvest logistics infrastructure [70], the position of C6 in seventh place suggests that SMEs primarily seek to achieve maturity in digital infrastructure and data management, with food waste prevention capability anticipated as an operational outcome of this technological transformation. This indicates that the integration of smart systems into operational processes to systematically reduce food loss is perceived as a more advanced stage in the transition towards a circular economy.

Although regulatory uncertainty and the lack of standardised metrics and measurement methods for a CE (C9), as well as government subsidies for enterprises to deploy Industry 4.0 technologies (C1), rank lower, they nonetheless retain structural importance. The literature indicates that uncertain regulations, lack of standards, and inadequate policy frameworks significantly hinder the implementation of CE practices [12,59,60]. While Ada et al. [60] point out that the absence of a robust legal framework to support these initiatives constitutes a major challenge, Stanescu et al. [12] emphasise that the lack of standardised approaches to performance measurement and progress evaluation is a significant barrier to the effective monitoring of circular initiatives. However, government incentives can play a facilitating role, particularly in overcoming financial and infrastructure barriers [10]. Within this framework, policymakers must establish clear and consistent regulatory frameworks, develop standardised measurement systems, and ensure long-term policy stability. Although incentive mechanisms are not sufficient on their own, when implemented alongside appropriate institutional structures [10,60], they can serve as important tools to accelerate transformation.

Finally, when these findings are evaluated, it becomes evident that the success factors for digitalisation in the transition process of agri-food SMEs towards a CE require a phased roadmap, particularly in the context of developing countries. The process begins with a technology- and data-driven approach, is subsequently supported by economic and organisational factors, and is ultimately complemented by environmental and institutional dimensions. Strategically, the initial phase necessitates prioritised investment in data analytics to boost agricultural output (C4), serving as the foundational driver for this digital transformation. Such investment enhances the accuracy of production forecasts, optimises resource use, and strengthens supply chain coordination. The second phase should address the challenge of high investment costs (C2), where grants, tax incentives, and public–private partnership mechanisms become critical. The third phase concerns data-driven collaboration, strategic data governance, and organisational learning (C5, C3). The effectiveness of these processes depends on firms’ capacity to engage in knowledge sharing and collaborative innovation across the supply chain, supported by employees’ motivation to acquire new knowledge and skills. In the fourth phase, environmental sustainability factors (C6, C7, C8) must be integrated into strategic planning, as they emerge as outputs of digital and organisational capacity and directly contribute to resource optimisation and waste reduction. As complementary dimensions of the roadmap, regulatory uncertainty and the lack of standardised metrics and measurement methods for a CE (C9), along with government subsidies for enterprises to deploy Industry 4.0 technologies (C1), retain their strategic significance. Clear regulations and coherent policy mechanisms facilitate the successful and sustainable adoption of digitalisation in the transition to a CE. This approach enables SMEs in the agri-food sector of developing countries to plan their digitalisation in the transition to a CE not merely as a technological investment, but as an integrated strategic process encompassing economic, organisational, and environmental dimensions.

The results of our study are consistent with the current agricultural transformation literature in emphasising the importance of technological tools (C4, C5) and organisational learning willingness (C3) [44]. However, in contrast to large-scale firms—typically resource-rich and beneficiaries of substantial government incentives—the motivational effect of government subsidies (C1) is relatively weak for agri-food SMEs in developing countries. Instead, high investment costs (C2) emerge as a significant operational bottleneck. This finding suggests that digital transformation strategies cannot adopt a “one-size-fits-all” approach and must be tailored to firm size (SMEs) and CE objectives.

6. Implications

6.1. Theoretical Implications

This study makes several theoretical contributions to the literature on digitalisation in the transition to a CE within agri-food SMEs. The findings support the applicability of the TOE framework in explaining digitalisation processes in the agri-food sector. Consistent with the TOE perspective, the results demonstrate that digital transformation in SMEs does not depend solely on technological conditions but arises from the combined effects of technological capability, organisational readiness, and environmental context. This aligns with previous research, which argues that SME digitalisation should not be regarded merely as a technological adoption decision, but rather as a multidimensional organisational process. This study extends the application of the TOE framework. The findings indicate that the relative importance of TOE dimensions may vary according to sectoral and contextual conditions. In comparison with the existing literature, the results demonstrate that the explanatory weight of the TOE dimensions is not fixed, but may change depending on sectoral structure, resource constraints, and sustainability pressures. Thus, the study strengthens the contextual interpretation of the TOE framework for agri-food SMEs under the pressure of circular transformation in developing countries.

The study also shows that the combined use of the PESTEL and TOE frameworks enables a more systematic explanation of digital transformation under CE conditions. In this research, PESTEL was utilised to identify and classify success factors, while TOE was employed to explain how these factors operate at the organisational level. This dual-framework approach provides a more integrated conceptual perspective for the agri-food sector. By contributing to the digital transformation literature concerning the transition to a CE among agri-food SMEs in developing countries, the study demonstrates that institutional and cultural contexts can influence how the TOE dimensions are shaped in practice. The Turkish context reveals that financial constraints, traditional management structures, and a cautious approach to technology can alter the relationship between technological opportunity and organisational adoption. This suggests that digital transformation models developed in advanced economies must be contextually reinterpreted when applied to SMEs in developing countries.

Finally, from a methodological perspective, the study contributes to the prioritisation of critical success factors under uncertainty by employing a fuzzy multi-criteria decision-making approach (SF-AHP). The integration of a theory-based classification structure with a fuzzy decision-making method enables more systematic and reliable analysis of complex digitalisation decisions in SMEs.

6.2. Practical Implications

The findings of this study also provide several practical implications for managers and policymakers seeking to accelerate digitalisation in agri-food SMEs during the transition to a CE. First, managers should view digitalisation not as an isolated technology investment, but as a holistic transformation process. The findings indicate that “Data Analytics to boost agricultural output” is the most critical success factor. This implies that firms need to prioritise systems that enable real-time data collection, predictive analytics, and resource optimisation. Accordingly, managers should not only invest in digital tools but also develop organisational capabilities that transform operational data into strategic decision-making inputs.

The prominence of high investment costs highlights the necessity for robust financial planning at the firm level, alongside targeted support mechanisms at the policy level. For many agri-food SMEs, the adoption of advanced technologies is constrained by limited capital, uncertain returns, and high implementation costs. Consequently, policymakers should introduce investment incentives, low-interest financing schemes, tax reductions, and public–private partnership models specifically tailored to the needs of small agricultural enterprises. Unless the financial burden is alleviated, a significant proportion of digital transformation initiatives may remain inaccessible to small-scale firms.

The importance of willingness to learn and technological competence emphasises the central role of human capital in digital transformation. Firms must establish continuous training systems to enhance digital literacy, sustainability awareness, and employee adaptability. This is particularly critical for agri-food SMEs, where decision-making processes are often centralised, and technology adoption largely depends on the attitudes of business owners. Therefore, the implementation process requires not only technological readiness, but also organisational readiness.

On the other hand, although IoT, AI, and blockchain technologies enhance traceability and efficiency, they may also introduce risks such as cybersecurity vulnerabilities, data privacy concerns, maintenance costs, and operational disruptions. Consequently, managers must integrate risk governance, digital security protocols, and infrastructure resilience into their implementation strategies. Successful digitalisation can only be achieved through the effective management of such systemic vulnerabilities.

Although regulatory factors appear to have a relatively lower level of statistical importance, the critical role of regulatory clarity should not be underestimated. SMEs are likely to postpone digital investments when legal requirements, performance metrics, and reporting standards remain uncertain. Therefore, governments should develop CE guidelines, sector-specific digital standards, and measurable sustainability indicators. A stable regulatory environment strengthens long-term planning and enhances managerial confidence.

Ultimately, the Turkish context of this study underscores the importance of acknowledging cultural barriers to digital adoption. In numerous agri-food SMEs, entrenched traditional business practices, risk-averse mindsets, and management styles centred on interpersonal relationships can impede the uptake of new technologies. Accordingly, practical support should extend beyond financial incentives to encompass advisory services, mechanisms for building trust, and sector-specific extension services aimed at mitigating resistance to organisational change. Digital transformation in agriculture is not solely a technical matter; it also constitutes a managerial and cultural transformation that necessitates coordinated institutional support.

7. Conclusions

This study identifies and prioritises the critical success factors influencing the digitalisation of SMEs in the agri-food sector in developing countries during their transition to a CE. The SF-AHP results indicate that investments in data analytics to boost agricultural output (C4) are of the highest priority. This is followed by high investment costs (C2), the availability of efficient technology for the CE process (C5), and the willingness to learn new knowledge or deploy Industry 4.0 technologies (C3). Environmental factors (C6, C7, C8) and institutional regulations or incentives (C1, C9), although ranked lower, are considered complementary dimensions of digitalisation and CE processes. The findings suggest that, in developing countries, agri-food SMEs should approach digitalisation in their transition to a CE through a phased strategy: first, by investing in technology and data-driven innovation; second, by undertaking economic and organisational preparations; and finally, by integrating environmental and institutional factors.

While this study has certain limitations, it also presents considerable opportunities for future research. The reliance on a limited number of expert opinions within the Turkish context restricts the generalisability of the findings. Repeating the study with a larger expert sample in future research could improve generalisability. The literature highlights that contextual factors—including organisational structure, infrastructure, and the regulatory environment—play a significant role in shaping digital transformation and CE practices. Consequently, comparative studies involving different countries and sectors would enhance the contextual depth and strengthen the generalisability of the results. Additionally, although this study analysed the priority structure among the critical success factors, it did not examine the causal relationships between them. Yet, the literature indicates that the relationships between digital technologies, organisational learning, and sustainable performance are multidimensional and interactive. In this regard, future research employing analytical techniques such as structural equation modelling to explore the complex relationships and priorities among factors would be valuable for uncovering these inter-factor dynamics. One of the main limitations of this study is that the criterion weights were calculated only within the framework of a fuzzy environment. Although the fuzzy approach offers a common and effective method for modelling uncertainty, there are also different uncertainty representation approaches in the literature [88,89]. In this regard, future research will allow for the comparative evaluation of the findings by analysing the same problem in neutrosophic and other uncertainty-based decision-making environments.