Classification, Evaluation and Adoption of Innovation: A Systematic Review of the Agri-Food Sector

Abstract

The transition towards sustainable agri-food systems requires understanding factors influencing innovation adoption across agri-food companies. This systematic literature review, following PRISMA methodology, examines innovation types, their intended purposes, and adoption determinants among worldwide stakeholders. Data were extracted from Scopus and Web of Science databases using rigorous selection criteria, covering publications from January 2014 to January 2025. From 775 initial records, 80 publications were selected for quantitative analysis, of these 74 empirical studies included in qualitative analysis. Innovations were categorized based on ecological, economic, social, and institutional purposes, revealing ecological purpose innovations predominated. Subsequently, adoption factors were classified using the tripartite framework based on extrinsic, intrinsic, and intervening variables. Findings reveal developing regions (Sub-Saharan Africa and Asia) representing 65% of studies. Agriculture sector dominated research attention, with cereals as the most investigated value chain, reflecting their fundamental role in global food security and nutrition. Analysis demonstrates that adoption decisions result from complex interactions between external structural conditions, individual psychological factors, and support mechanisms. Results underscore the context-dependent nature of innovation adoption and the need for context-sensitive, multi-stakeholder approaches facilitating sustainable and digital food system transformations.

1. Introduction

In recent years, the policies promoted in favour of the ecological, digital, and sustainable food systems transition (e.g., the European Green Deal, the Farm to Fork strategy, the United Nations Sustainable Development Goals), as well as funding programs for research and innovation such as Horizon Europe (2021–2027), have proposed a new model of growth and economic development that is smarter, more sustainable and more inclusive. According to the FAO Statistical Yearbook 2024, global agricultural value has increased by 89 percent in real terms over the past two decades, reaching $3.8 trillion in 2022, while the proportion of the global workforce employed in agriculture has decreased from 40 percent in 2000 to 26 percent in 2022 [1]. The new Common Agricultural Policy programming (2023–2027), supporting sustainable farming and innovation, has established, among its thematic objectives, the transversal goal of modernizing agriculture and rural areas by promoting and sharing knowledge, innovation, and digitalization in agriculture, with the European Commission allocating more than €200 million through Horizon 2020 for research and innovation in digital technologies for the agricultural sector, and an additional €762.7 million assigned through the Digital Europe work programs for 2024 [2].

Agri-food supply chains represent interconnected networks that intermediate the flow of products between largely rural farmers, fisherfolk, or herders and increasingly urban consumers, serving as key loci for technology transfer and foreign investment [3]. In this context, innovation adoption refers to the complex process by which farmers, agri-food companies, and other supply chain actors integrate new technologies, practices, or organizational methods into their operations to improve efficiency, sustainability, and competitiveness. This definition encompasses a broad spectrum of innovations, from technological advancements like precision agriculture and digital tools to novel practices such as conservation agriculture and new forms of collaborative business models, aligning with the multi-dimensional nature of innovation in agricultural systems [3,4,5]. Agricultural sustainability encompasses systems that aim to make the best use of environmental goods and services while not damaging these assets, integrating biological and ecological processes into food production while minimizing harmful non-renewable inputs and making productive use of farmers’ knowledge and collective capacities [6]. The OECD-FAO Agricultural Outlook 2025–2034 projects that global agricultural and fish production will increase by 14% over the next decade, primarily driven by productivity gains, particularly in middle-income countries. However, increases in productivity will help lower emission intensity and keep the anticipated rise in direct agriculture GHG emissions to 6% [7]. These projections underscore the critical role that innovation adoption plays in addressing future challenges concerning sustainability and business competitiveness [8].

Despite the recognized importance of innovation adoption for achieving sustainable development goals and enhancing agri-food system resilience, significant challenges persist in understanding and facilitating uptake across varied agricultural contexts. Adoption rates remain below expectations in many countries, with considerable heterogeneity in innovation uptake patterns across different types of value chain, innovations, and geographical contexts. This variety is compounded by the multiple nature of agri-food supply chains, where innovation adoption decisions are influenced not only by individual characteristics but also by supply chain dynamics, collaborative relationships, and institutional factors.

Previous systematic reviews have identified critical limitations in the current understanding of innovation adoption processes. For instance, Rosario et al. [9] highlighted the inability to compare the most utilized models and types of sustainable innovations to evaluate whether different innovations are represented by the same adoption or learning process. Similarly, Giua et al. [10] emphasized the need to extend focus beyond individual dimensions to include organizational factors relative to supply chains where farmers operate, noting that actors and organizational factors might play important explanatory roles in understanding adoption and diffusion across the entire agri-food sector’s digitalization. Vecchio et al. [11] demonstrated that context related variables create positive “cultural” environments favouring innovation adoption, particularly through territorial systems of innovation that engender both geographical and organizational proximity. More recently, Rizzo et al. [12] and John et al. [13], in their systematic literature review, have emphasized the need for more comprehensive approaches to understanding farmers’ adoption of sustainable and digital innovations respectively, particularly highlighting the role of social dynamics, awareness levels, and knowledge dissemination pathways.

According to Meijer et al. [5] research on innovation adoption in agriculture has traditionally emphasized extrinsic variables such as technology characteristics and external environmental conditions, with less attention paid to intrinsic variables including knowledge, attitudes, and perceptions of potential adopters, and limited consideration of intervening variables within the process such as extension and communication mechanisms. While this comprehensive framework was originally used for understanding agroforestry adoption among smallholder farmers in sub-Saharan Africa, integrating extrinsic, intrinsic, and intervening variables in the decision-making process, its broader application across diverse geographical contexts, innovation types, and agricultural systems remains underexplored. Similarly, the multifunctional approach to agricultural sustainability that recognizes systems as jointly producing food and contributing to public goods such as clean water, wildlife habitats, and carbon sequestration [6] has been not widely applied in empirical research examining innovation purposes. Furthermore, despite recognition that agri-food value chains serve as key loci for technology transfer and foreign investment [3], systematic analysis of how innovation adoption patterns vary across different value chain innovations and geographical contexts remains limited. Comprehensive systematic reviews integrating innovation types, purposes, and adoption factors across multiple dimensions and supply chain contexts remain absent from the literature in the moment in which this work is carried out by authors. To address this knowledge gap, this study contributes to building crucial knowledge for policymakers who can use the results to design more targeted and effective policies in promoting agricultural innovation adoption, ultimately supporting global sustainability objectives and food system transformation goals.

The present study aims to achieve several strategic objectives, focusing primarily on identifying the types of innovations examined in agri-food adoption research. These innovations are categorized according to their intended purposes within sustainability frameworks, building on Pretty’s [6] principles of agricultural sustainability that emphasize the integration of biological processes, minimization of harmful inputs, and productive use of farmers’ knowledge and collective capacities. Innovations are classified across four dimensions: ecological, economic, social, and institutional purposes, following the multifunctional approach to agricultural systems that jointly produce food and contribute to valued public goods. Additionally, this research examines the determinants influencing innovation adoption decisions, utilizing the analytical framework proposed by Meijer et al. [5] that recognizes the multiple interplay between extrinsic variables (characteristics of the technology and attributes of the external environment), intrinsic variables (knowledge, perceptions, and attitudes of potential adopters), and intervening variables (extension, training, and communication mechanisms that are crucial in developing knowledge, perceptions and attitudes) in the decision-making process. Particular attention is given to understanding how these adoption determinants operate across different innovation types and geographical contexts within agri-food supply chains, recognizing the role of agri-food value chains as intermediaries that facilitate technology transfer and structural change in agricultural systems among supply chain actors [3], excluding final consumers.

Accordingly, the systematic review addresses three research questions: (RQ1) What types of innovation are evaluated in the agri-food or agricultural sectors’ adoption? (RQ2) What purposes and objects have been investigated to evaluate innovation adoption of agrifood innovations? (RQ3) What factors affect farmers’ or stakeholders’ propensity to adopt innovations?

The systematic review contributes to the scientific literature by providing a comprehensive mapping of innovation types and their associated adoption determinants across the three domains of influence, identifying patterns in how different categories of innovations address sustainability challenges within agri-food value chains that serve as critical intermediaries for technology transfer. This comprehensive approach advances theoretical understanding of the mechanistic interactions between extrinsic, intrinsic, and intervening variables that drive adoption decisions while providing practical insights for targeting technologies appropriately to ensure sustainability across varied agricultural contexts.

2. Materials and Methods

To answer the research questions and provide new scientific insights into multidisciplinary research approaches, the PRISMA method by Page et al. [14] was adopted to ensure a transparent and rigorous approach. The flowchart of search process follows the four steps recommended by this protocol’s guidelines: identification, screening, eligibility, and data extraction (Supplementary Material: Table S1: PRISMA 2020 Checklist).

2.1. Identification

The documents’ identification was performed using the Scopus citation database, published by Elsevier, along with the Web of Science, selected for its extensive interdisciplinary coverage of high-quality, peer-reviewed literature in the social and agricultural sciences. The search strategy combined keywords and Boolean operators, developed through an analysis of previous systematic reviews on innovation adoption in agriculture [9,12,13] and two focus groups involving university researchers, rural development experts and stakeholders from the agri-food sector such as farmers, representatives of producer organizations and cooperatives, agro-industrial firms, and distributors.

The final list of keywords useful to develop the search strings included: “Innovation”; “Agri-food”; “Assessment”; “Measurement”; “Evaluation”; “Propensity”; “Inclination”; “Attitude”; “Willingness”; “Predisposition”; “Driver”; “Barrier”; “Determinant”; “Behavio*”; “Intention”; “Accepta*”; “Adopt*”; and “Agriculture”.

Finally, the following filters were set:

• Year: from 2014 to 2025.
• Document type: articles and reviews.
• Publication stage: final.
• Language: English.

In

Table 1. Research protocol for each database.

Category	Description
Search string	Agri-food OR Agriculture AND Innovation AND Assessment OR Measurement OR Evaluation AND Propensity OR Inclination OR Attitude OR Willingness OR Predisposition OR Driver OR Barrier OR Determinant OR Behavio* OR Intention OR Accepta* OR Adopt*.	Agri-food OR Agriculture AND Innovation AND Assessment OR Measurement OR Evaluation AND Propensity OR Inclination OR Attitude OR Willingness OR Predisposition OR Driver OR Barrier OR Determinant OR Behavio* OR Intention OR Accepta* OR Adopt*.
Document type	Article and review	Article and review
Database	Scopus	Web of Sciences
Period	January 2014–25 January 2025	January 2014–25 January 2025
Language	English	English
Publication stage	Final	Final

Source: own elaboration.

are reported the complete search strings applied to “Title, Keywords, Abstract” fields, and the specific filters about period, language, publication stage and type for each database extraction. The time frame between 2014 and 2025 was chosen to capture recent developments shaped by major policy frameworks (the European Green Deal, the Farm to Fork strategy, the United Nations Sustainable Development Goals) and technological advancements, as recommended by Ofosu-Ampong et al. [15], who identified this period as a significant turning point in agricultural behaviour change research. Thus, this interval was deemed appropriate to encompass the most relevant conceptual and methodological advancements in the field. By applying the above criteria, 775 publications were identified.

2.2. Screening

The initial search conducted in Scopus and Web of Science databases produced 775 results. After removing duplicates and applying filters in Table 1, 413 studies were retained for the screening phase. In this stage, documents were assessed based on the title, abstract, and keywords, and no subject area filters were applied, to avoid prematurely excluding potentially relevant contributions from fields outside economics or the social sciences. This inclusive and multidisciplinary approach was intentionally adopted to capture evidence across a broad disciplinary spectrum, potentially offering valuable insights into the intrinsic and extrinsic factors influencing innovation adoption by actors within agri-food systems. Papers from disciplines such as medicine or engineering were included only if aligned with the research questions. Studies without a specific focus on agri-food innovation adoption were excluded. In doubtful cases, the research team applied a cautious and inclusive approach, preferring to retain studies for the next review stage rather than risk discarding potentially useful contributions. After screening of title, abstract and keywords, 154 publications met the eligibility criteria.

2.3. Eligibility

Publications were further screened by fully reading the contents of the articles. At this stage, two researchers independently reviewed the 154 publications, resulting in 80 studies selected for final systematic analysis. All metadata (including details such as reference type, authors, year, reference, paper title, link, abstract, and keywords) were exported into an Microsoft Excel 365 spreadsheet to facilitate subsequent screening and eligibility assessments based on the specified criteria (Supplementary Material: Table S2: Studies included in review).

• Criterion 1. Specific to the agricultural or agri-food sector
• It was included in this analysis only those publications that conduct research specifically within the agricultural or agri-food fields [16]. If a document merely references the relevant terms but focuses on a different sector, such as health or education, it was excluded.

• Criterion 2. Specific to innovation adoption
• It included only those publications that specifically examine the process of innovation adoption [9] within agricultural or agri-food enterprises’ contexts. If a document merely mentions innovation without addressing its adoption dynamics or the factors influencing it, it was excluded.

• Criterion 3. Exclusion of studies focused exclusively on impact measurement
• Studies that base their analysis primarily on measuring impacts were excluded, as the objective is to explore the factors that promote innovation adoption in agri-food enterprises rather than to evaluate post-adoption outcomes or policies’ impact evaluations. Given the complexity and breadth of impact evaluation, a separate systematic review would be required to comprehensively address this aspect [16]. Moreover, including such studies would dilute the review’s focus by diverting attention from the mechanisms and dynamics that drive the innovation adoption process.

• Criterion 4. Stakeholder dynamics in innovation adoption
• Studies that provide feedback on the dynamics involving key stakeholders, such as farmers, managers, and other individuals involved in innovation adoption, or that contribute relevant insights to this aspect and category were included. Studies that do not address these aspects, particularly those focused on the consumer perspective, were excluded.

The criteria adopted in this systematic review, following the PRISMA 2020 methodology [14], focused on identifying studies that examine the factors influencing the adoption process. This approach aimed to ensure a comprehensive and multidisciplinary understanding of the drivers and barriers to innovation adoption within agricultural and agri-food enterprises.

2.4. Data Extraction, Quantity and Qualitative Analysis

Following the eligibility phase, metadata from the 80 included studies were exported into an Excel spreadsheet to continue the analysis phase.

Quantitative analysis focused on:

• Year of publication, to trace the temporal distribution of the selected studies during recent changes driven by European policies such as the European Green Deal and the new Common Agricultural Policy programming.
• Geographical focus, identifying the country or countries in which the study was conducted. When multiple countries were addressed within a single study, each country was recorded as a separate unit of analysis.
• Sectoral and value chain focus, to investigate the distribution of innovations across four sectors (agriculture, livestock, agri-food, and forestry) and to map specific value chains when identified. This allowed for a better understanding of the context and scope in which innovation is analyzed.

Qualitative analysis focused on:

• Purpose of each type of innovation, classified into thematic categories reflecting their main aims (ecological, social, economic, and institutional) and stated aims of the selected articles, to understand the primary research objectives addressed by the existing literature. This categorization was developed through a conceptual framework (Figure 1) derived from an analysis of the literature, which allowed the reviewed innovations to be grouped according to their intended purpose. Indeed, the research employs Pretty’s [6] and Barrett et al. [3] multifunctional sustainability principles to categorize innovations according to their ecological, economic, social, and institutional objectives, recognizing that agricultural systems can simultaneously produce food as well as contribute to preserving public goods, with a view to sustainability and innovation. Each of the four macro-categories includes specific subcategories that reflect the underlying goals and motivations behind innovation adoption. For example, biodiversity preservation and climate-smart agriculture fall under the ecological dimension, while productivity and yield improvement or rural livelihoods are considered as economic dimensions. Given the complex nature of agricultural innovation, some innovations were associated with more than one purpose or subcategory. This classification was manually applied across the dataset and enables an understanding of how agricultural innovations are positioned within different dimensions of sustainability, highlighting the relative prevalence of each category in the current literature.
• Publications were assigned to multiple categories when their respective innovations served different purposes or when studies explicitly addressed multiple objectives. Each categorical assignment received one point, reflecting the multi-dimensional nature of agricultural innovations. The classification framework employed a dual-criterion approach, grounding categorization decisions on either: (i) explicit research objectives stated in the papers, (ii) inherent technical characteristics of the innovations, or (iii) convergent alignment of both dimensions.
• Factors affecting innovation adoption, which were identified, coded, and classified as intrinsic, extrinsic, or intervening variables. Their relevance was then analyzed in relation to innovation types, geographical locations, and agricultural system domains. This classification contributes to addressing knowledge gaps related to the multiple and context-specific nature of innovation adoption, as highlighted in recent studies. The study adopts a comprehensive approach that systematically examines innovation adoption based on the vision of Meijer et al.’s [5] framework, which emphasizes the complex interplay between extrinsic, intrinsic, and intervening variables in farmers’ decisions to adopt new agricultural technologies or practices (Figure 2). This review extends the original tripartite framework beyond its sub-Saharan African agroforestry context to encompass factors affecting innovation adoption decision making process within diverse geographical settings, innovation types, and agricultural systems.

Some of the records retrieved through the search strategy included literature reviews. These studies were read and included in quantitative analyses (temporal, geographical, and sectoral distribution) because they were considered to support the full understanding of research interest on the topic. However, they were excluded from all analyses requiring the identification and classification of specific themes such as innovation purposes and adoption factors. This decision was adopted because including review papers in qualitative analysis would have increased the risk of overlap or data distortion, as their contents often summarize evidence from primary studies already included in the sample.

Their exclusion ensures the direct relevance of the evidence analyzed, strengthens the validity of the results, and improves the transparency of the study selection process.

The adopted classification showed in Figure 1 distinguishes four main purposes, each comprising a set of more specific categories:

• Social purpose: farmer adoption and knowledge diffusion, food security and nutrition, gender, and inclusivity, cultural acceptance;
• Economic purpose: productivity and yield improvement, cost reduction and efficiency, market access, and value chains, rural livelihoods;
• Ecological purpose: reducing externalities, climate-smart agriculture, soil and water conservation, biodiversity preservation;
• Institutional purpose: policy frameworks, multi-stakeholder collaboration, standards, and certifications.

This framework enabled a structured interpretation of the innovations’ objectives and offered a means to explore the multiplicity of purposes and expected outcomes associated with the adoption of agricultural innovations in different contexts.

The conceptual model presented in Figure 2 illustrates the decision-making framework as a dynamic system with the farmer positioned as the central actor. The visual representation shows how three distinct categories of variables interact through directional flows to influence adoption outcomes.

Extrinsic factors encompass three key dimensions: farmer characteristics (e.g., age, education, farm size), environmental conditions (including governance and infrastructure aspects), and innovation characteristics (costs and benefits, technical and functional aspects). These external variables create the structural context that shapes the adoption environment. Intrinsic factors include the internal psychological and cognitive processes of potential adopters (attitudes toward innovation, risk perceptions, knowledge levels, and subjective norms) that directly influence individual decision-making. Intervening factors comprise extension services, training programs, and communication aspects that affect knowledge transfer and capacity building.

The framework’s directional arrows illustrate the dynamic relationships between these domains: extrinsic factors influence intrinsic variables, while intervening factors serve as facilitating mechanisms that can enhance or moderate these interactions. This tripartite visualization provides the analytical structure for systematically examining how different variables combine to influence farmers’ innovation adoption decisions across the diverse contexts analyzed in this review.

The comprehensive study selection process, which includes all the methodological steps (identification, screening, eligibility, and data extraction), is summarized in Figure 3.

3. Results

The PRISMA method [14] for the systematic literature analysis enabled a quantitative and qualitative classification of the most relevant advances in socio-economic and scientific research on innovation adoption in the agrifood sector to respond to the research questions. This section presents the results of the systematic analysis, focusing on 80 publications that explored innovation adoption in agriculture shaped by contextualized factors, typology of innovation, and variable affecting innovation adoption individuating through all considered publications.

3.1. Quantity Analysis

The temporal distribution of the selected studies reveals a progressive increase in the number of publications addressing innovation adoption in the agrifood sector. As shown in Figure 4, the review includes studies published from 2014 to January 2025. The number of contributions remains limited between 2014 and 2017, followed by a clear upward trend starting in 2018. Excluding two slight fluctuations in 2019 and 2023, the number continues to grow, reaching a peak at 16 in 2024. Only 1 study is recorded for 2025, reflecting the partial coverage of the first month of the year at the time of data collection.

To address the recurring call for greater contextualization in innovation adoption studies, this review systematically examines the country, sector, and value chain associated with each paper. The geographical distribution of the reviewed studies is presented in Figure 5, which reports the frequency of analyzed publications by country. Country-level context was identifiable in 74 out of 80 records. There were studies in which the research was conducted across multiple countries: Ethiopia, Nigeria, and Tanzania [17]; Senegal and Mali [18]; Kenya, Ethiopia, Tanzania, and Uganda [19]; Kenya and Tanzania [20]; and The Netherlands, United States, Saudi Arabia, and India [21]. In these cases, each country was counted individually. This approach allowed for a more comprehensive mapping of the geographical scope and ensured consistency with the review’s objective of contextualizing innovation adoption processes within local settings.

Countries classified as developed according to the World Bank 2024 [22] are shown in blue, while those classified as developing or emerging are shown in orange. Overall, the dataset includes 41 countries, comprising 27 developing countries and 14 developed ones. This indicates that developing countries appear with a frequency nearly twice that of developed countries. The United States and Tanzania are the most represented, with 6 studies each. Among the developed countries, the United States ranks first (6 studies), followed by Greece and Germany (3). All other developed countries are represented by a single study.

In contrast, among the developing countries, several are the focus of multiple publications: Tanzania (6), Pakistan (5), and Brazil, China, Ethiopia, India, and South Africa (4 studies each). Kenya follows with 3 studies. The remaining countries appear with lower frequencies, often represented by a single study. These results highlight a wide geographical coverage with a predominance of studies conducted in developing countries.

A complementary view of the previous analysis is provided by Figure 6. This map visualizes the spatial distribution of the reviewed studies by nuancing each country according to the number of publications attributed to it, with colour intensity ranging from 1 publication to 6 publications, as indicated in the legend. The map based on the same data presented in Figure 5 provides a spatial representation that facilitates the visual identification of coverage gaps and geographical clusters following in

Table 2. Distribution of selected publications by macro-region. Overview of the number of studies conducted in each macro-region, showing a clear predominance of research in Sub-Saharan Africa and Asia.

Macro-Region	N. Publications
Sub-Saharan Africa	27
Asia	24
Europe	16
Latin America	8
North America	6
Middle East	1
North Africa	1
Oceania	1

Source: own elaboration.

.

The figure shows that a limited number of countries, such as the United States, Tanzania, Pakistan, and Brazil, stand out with higher nuancing intensity; while a substantial portion of the map is covered by light shading or remains unshaded. This visually reinforces the concentration of research in a small group of countries and the sparse or absent representation in developed regions, particularly in Europe (excluding a few Western countries) and developing regions of Latin America (with some exceptions such as Brazil and Mexico), and parts of Asia and Africa.

To provide a comprehensive worldwide perspective, Table 2 aggregates the country-level data into macro-regions. This aggregation aims to highlight regional research trends and their alignment with areas facing major sustainability challenges. Macro-regions were defined following the World Bank 2024, supplemented by FAO frameworks where appropriate. This methodological choice ensures consistency with internationally recognised standards and supports the comparability of findings across different geographical contexts. Sub-Saharan Africa (Tanzania, Ethiopia, South Africa, Kenya, Mali, Senegal, Uganda, Ghana, Malawi, Nigeria, Togo) and Asia (Pakistan, China, India, Bangladesh, Indonesia, Malaysia, Nepal, Cambodia, Japan and Laos) emerge as the most frequently represented regions, with 27 and 24 studies, respectively. Together, these two macro-regions account for approximately 65% of all reviewed publications. Europe (Greece, Germany, Austria, Belgium, Czech Republic, France, Hungary, Ireland, Italy, The Netherlands, Spain and Ukraine) contributes 16 studies, followed by Latin America 8 studies (Brazil, Mexico, Colombia, and Costa Rica) and North America with 6 publications from only United States, followed by the Middle East (Saudi Arabia), North Africa (Tunisia), and Oceania (Australia) with a single study each one. These macro-regions with minimal representation were also included in the analysis to ensure completeness, although their limited number of studies (one each) prevents broader generalizations.

The reviewed studies were also examined in relation to the specific sector and the value chain investigated. Information about the sector was available in 79 out of 80 records, with one study (49) lacking sufficient detail to allow classification. Among the 79 identified cases, a total of 81 sectoral references were coded, as three studies addressed more than one sector. In these cases, each sector was considered individually. This methodological choice is consistent with the objective of this review, which seeks to contextualize innovation adoption within the agricultural and agri-food system. The sectors most frequently represented are agriculture (61 studies), agri-food (10), livestock (9), and forestry (1).

In parallel, value chains were identified and analyzed in 39 studies, where explicit reference to a specific commodity chain or product system was made. When multiple value chains were mentioned in a single study, the most prominently addressed one was selected for classification. The most represented value chains were cereals (19 studies), which include a variety of staple crops such as wheat, maize, rice, millet, and Kernza. These were followed by fruits and vegetables (7), cotton (6), poultry (5), cattle, and legumes including soybean and beans (4 each), coffee, dairy, and oil seeds as olives and rapeseed (3 each), aromatic and ornamental plants and pig (2 each), and cacao, carp, and insect (1 each). This distribution is graphically illustrated in Figure 7, which presents the value chains in descending order of frequency. When considered together, value chains related to animal production (poultry, pig, and cattle) account for a total of 11 studies, placing livestock-related chains the second most represented group after cereals in terms of research attention in innovation adoption. This approach allowed mapping the distribution of research interest across different agricultural and food related value chains. The inclusion of sector and value chain information responds directly to one of the gaps highlighted in the literature, namely the need to better integrate local context and production systems in the analysis of innovation adoption processes.

3.2. Qualitative Analysis

3.2.1. Mapping Innovation Typologies According to Their Intended Purposes

In line with the conceptual framework presented in Figure 2, the systematic analysis included 70 publications that explicitly referred to a single or group of specifically mentioned innovations in agri-food. In the remaining 10 studies excluded from analysis, six of these were removed because reviews, while in the other cases research investigation explored wider issues without linking them to specific technologies or practices. Their exclusion enables the analytical relevance of the proposed classification, which was designed to describe the purposes that each innovation is intended to reflect.

Therefore, 70 publications and relative investigated innovations presented a complex distribution across four purposes connected sustainability and governance dimensions. This classification allows understanding how agricultural innovations are positioned within different dimensions considered in this analysis and highlights the relative prevalence of each category in the current literature.

The radial chart illustrated in Figure 8, places the four purposes at the centre and graphically shows the corresponding subcategories at the outer level. The ecological purpose innovations were the most prevalent in the studies with 61 assignments, with strong attention to reducing externalities (n = 21), while less emphasis was given to climate-smart agriculture (n = 16), soil and water conservation (n = 15), and biodiversity preservation (n = 9). These were followed by well-balanced economic and social purposes with 48 and 47 assignments, respectively. Economic purpose innovations (n = 48) were distributed across productivity and yield improvement (n = 21), cost reduction and efficiency (n = 16), market access and value chains (n = 7), and rural livelihoods (n = 4). While social purpose innovations (n = 47) showed concentrated distribution, with farmer adoption and knowledge diffusion representing the most of all categories (n = 34), followed by food security and nutrition (n = 7), gender and inclusivity (n = 4), and cultural acceptance (n = 2). In contrast, institutional dimensions (n = 29) are significantly less explored, with three specific thematic categories: policy frameworks (n = 13), multi-stakeholder collaboration (n = 8), and standards and certifications (n = 8).

The results presented in

Table 3. Detailed classification of each agricultural purpose innovation involved in the analysis (social, economic, ecological, institutional), with related score. The third column reports the frequency of innovation associated with each group.

Cluster	Categories	Score
Ecological	Biodiversity preservation	9
	Climate-smart agriculture	16
	Reducing externalities	21
	Soil & water conservation	15
Economic	Cost reduction & efficiency	16
	Market access & value chains	7
	Productivity & yield improvement	21
	Rural livelihoods	4
Institutional	Multi-stakeholder collaboration	8
	Policy frameworks	13
	Standards & certifications	8
Social	Cultural acceptance	2
	Farmer adoption & knowledge diffusion	34
	Food security & nutrition	7
	Gender & inclusivity	4

Note: Multiple labelling was applied to account for innovations with multiple purposes. Source: own elaboration.

support above mentioned categorization, confirming the significant variation in thematic distribution: ecological purposes dominate the research, with strong attention to reducing externalities and promoting climate-smart practices. Moreover, ecological and economic purposes demonstrated a good balance across multiple categories, with no single category dominating beyond 21 innovations within each dimension. On the contrary, social purpose innovations showed the highest frequency, in the farmer adoption and knowledge diffusion category, with a total score of 34 out of 47 innovations, and the lowest frequency of all records in “Cultural acceptance” with a score of only 2 out of 47. Institutional purposes showed moderate concentration, with policy frameworks representing 13 out of 29 institutionally oriented innovations.

Cross-dimensional analysis identified innovations investigated in current literature serving multiple sustainability purposes at the same time efficiently. Precision agriculture and decision support technologies were prominently represented across ecological purposes with their contribution in favour of environmental impact minimization, as exemplified by studies examining decision support tools for fertilizer application [23] and geospatial decision support tools for conservation practices [24]. In the “Productivity & yield improvement” category, agriculture 4.0 implementation approaches [25,26,27,28] dominate, including modern instruments for the agricultural context like Internet of Things (IoT) platforms adoption [29], smart glasses for augmented reality applications [30], and agricultural advice apps [31]. Finally, precision agriculture innovations demonstrate the increase of “Farmer adoption & knowledge diffusion” processes. In fact, advanced sensing technologies, IoT platforms, and augmented reality applications [29,30,32,33], can contribute to more socially embedded and effective innovation ecosystems, as well as enterprise resource planning (ERP) systems [34,35] provide organizational infrastructure for technology integration within worldwide enterprises.

Conservation agriculture systems and sustainable management practices dominated two categories in ecological purpose: firstly, “Soil & water conservation” [36,37,38,39,40,41] with studies examining farmers’ behavioral attitudes toward conservation agriculture adoption [37], performance evaluation of conservation agriculture components on a 10 year period in Mexican farms [39], and perceptions towards conservation agriculture amongst smallholder farmers [41]. Secondly, “Biodiversity preservation” in which some papers examine behavioral attitudes [37], perceptions and intention towards these sustainable agricultural practices amongst smallholder farmers of different developing country [40,41,42]. In addiction conservation agriculture approaches [20,36,41,43] are included in “Farmer adoption & knowledge diffusion” demonstrating many efforts to promote sustainable soil and crop management practices by research, often requiring assessment tools and learning processes to overcome adoption barriers [38] by strengthening integrated efficiency practices included sustainable approaches such as innovative climate change mitigation practices and conservation agriculture systems [37,39,40,44], and agricultural machinery adoption for mechanization efficiency [45]. In this context can be inserted climate-smart agriculture designed innovations and practices addressing climate change challenges through adaptation and mitigation strategies. Very clear examples are the study on climate smart agriculture implementation in favour of a more sustainable coffee production system [46], while research on climate-smart villages [47] examined institutional frameworks for promoting climate adaptation enhancing collaboration at community levels. These innovations had inherent climate adaptive characteristics of the involved technologies in mitigating clime change impacts, aligned with research objectives explicitly targeting to investigate climate resilience [47,48], risk mitigation [44,49,50], and sustainable agricultural transformation under changing environmental conditions [46,47,51,52,53].

The results reveal that the most significant score is attributed to “Farmer adoption & knowledge diffusion”, demonstrating this work’s alignment with research objectives. In fact, it emerged as the most prevalent subcategory within the purpose classification, with 34 assignments across publications. This group includes studies unified by their focus on bridging the gap between innovation availability and actual farmer adoption, providing insight regarding behavioural, structural, and contextual factors that influence adoption decisions and knowledge transfer mechanisms within the agrifood value chain, reinforcing farmer knowledge learning and opportunities for agricultural dissemination. Three categories represented the most unexplored categories in current literature about agricultural innovation adoption: “Rural livelihoods”, “Gender & inclusivity” and “Cultural acceptance”.

Within the economic purposes “Rural livelihoods” category the only 4 innovations had in common an approach centred on improving the adaptive capacity and resilience of rural farming systems through the integration of participatory development strategies and appropriate technical solutions. For instance, it included tropically adapted improved breeds (TAIBs) that analyse technical, economic, social, and environmental feasibility under smallholder management conditions [17]. Similarly, improved technology adoption in traditional poultry farming (ITTPF) among smallholder farmers in Togo [54] to strengthen existing livelihood systems. The psychological dimensions of sustainable adoption are explored through agroforestry innovations that aim to improve the livelihoods of smallholders in Ethiopia [42]. Finally, new forms of cooperation create cooperative systems that ensure small-scale farming sustainability while strengthening local food supply systems in Austria [55], representing organizational innovations that enhance community resilience through market integration.

The “Gender & inclusivity” and “Cultural acceptance” categories comprise 4 and 2 studies, respectively. The first examines the inclusivity of agricultural innovations in Ethiopia [56], and how gendered roles and responsibilities influence agricultural innovation adoption and their perception. These studies reveal the critical importance of understanding demographic factors, particularly gender roles in different contexts. Gender topic implications are revealed concerning drought-tolerant maize adoption [50] and climate-smart push-pull technology for controlling stemborers [19] in sub-Saharan Africa, further illustrating how gendered roles and responsibilities influence innovation adoption, revealing opportunities to address gender based differences still too little studied. The second group explores how social innovation capacity and new forms of cooperation facilitate the adoption of agricultural innovations within specific cultural and geographical contexts, contributing to sustainable transformation. Research on private-public collaboration models [57] explored the establishment of alternative food networks through local food stores with regional products, examining how proximity and social innovation capacity enable sustainable transformation in rural areas. This organizational innovation approach is complemented by a study on creating cooperative systems which ensure the sustainability of small-scale farming [55], which illustrates various forms of cooperation between farmers and consumers that ensure the sustainability of local food supply systems.

Complementing these cross dimensional insights, the institutional purpose across its three dimensions (multi-stakeholder collaboration, policy frameworks, and standards & certifications) highlighted significant overlaps with the categories previously linked to sustainability dimensions. These complementarities emerged through the adoption of numerous digital technologies, addressing a successful integration within agricultural systems (e.g., [31,51]). For instance, the blockchain technology adoption [21] addressed agricultural supply chain comparing the trends in developed and developing economies, while research on Internet of Things platforms [29] contributes to understanding willingness to adopt and innovation resistance in the context of information system research. These studies emphasize the need for institutional support for digital transformation while addressing behavioral and cultural dimensions of technology adoption. At the same way, the adoption of sustainable innovation like conservation agriculture, eco-innovation, enterprise resource planning systems and climate smart agricultural technologies [20,34,35,36,47,58] can enhance collaboration in favour of climate adaptation involving multiple actors with either similar or different roles. In this context, the framework for sustainable transformation of smallholder farming systems [43] integrates technical innovations with social realization pathways, promoting overcoming adoption barriers and recommending best practice bundles. Moreover, these complementarities were also evident in the presence of organizational innovations’ adoption, including new forms of interaction among stakeholders and new public–private collaboration models designed to foster sustainable transformation [21,59]. Creating new forms of cooperative systems [55] focuses on establishing various forms of collaboration among farmers and with consumers that ensure the sustainability of small-scale farming through local food supply systems. Alternative food networks adoption [57] focuses on the concept of proximity to understand public-private collaboration models that aim at facilitating sustainable rural areas transformation through local food stores with regional products. Finally, innovation network systems [60] represent institutional innovations aimed at ensuring fair representation of the diversity of farmers’ views, addressing the challenge of articulating them, and maintaining the focus on how farmers’ interests can be articulated in innovation processes.

3.2.2. Mapping the Main Factors That Influence the Innovation Adoption Process Across Own Contexts

The analysis of the 74 articles included in this systematic literature review aimed to identify the key factors influencing the adoption process of actors involved and to link them within their own context. Among these, 57 studies explicitly reported one or more influencing factors of decision-making process in their findings, allowing for a comprehensive classification based on the conceptual framework in Figure 3. This approach enabled the extraction of multiple factors within individual studies, resulting in a mapping of variables affecting innovation adoption. Particular attention was given to the evidence resulting from comparing studies conducted in developed and developing countries, as well as to the specific typologies of the innovations considered. This analysis has enabled a deeper understanding of the multidimensional and context dependent nature of innovation adoption processes in the agrifood sector. The following paragraph presents the evidence emerging from the selected studies by grouping the factors into extrinsic and intrinsic factors related to adopters, underlining their interaction in decision-making process, and the crucial role which may have extension and training in affecting the implementation of a new practice or technology [5].

The results are mentioned hereinafter across the three categories: extrinsic and intrinsic factors, and variables related to communication and extension intervening in the decision-making process of innovation’s adoption.

Table 4. Frequency of factors influencing innovation adoption classified by typology and category, with breakdown by country status (developed vs. developing). The table shows the frequency with which each factor, identified within the reviewed studies, was associated to relative category, distinguishing own geographical context between developing and developed countries.

Typology of Factor	Subcategories and Levels	Developing Countries	Developed Countries	Score
Extrinsic	Farmer characteristics	34	9	43
	Socioeconomic characteristics	16	3
	Social networks	7	3
	Personal characteristics	7	2
	Familiarity with technology	2	0
	Status characteristics	1	0
	Personality characteristics	1	1
	Innovation characteristics	23	11	34
	Technical/functional aspects	9	5
	Benefits	7	4
	Costs	7	2
	External environment	22	7	29
	Political governances	8	3
	Geographical settings	8	2
	Market governance	4	1
	Societal culture	2	1
Intrinsic	Attitudes	30	18	91
	Perceptions	28	6
	Knowledge	6	3
Intervening	Communication and support mechanisms	19	8	27

Source: own elaboration.

provides a breakdown based on the factors’ frequency extracted from the relevant studies, sorted according to the main group’s specific category, extrinsic, intrinsic, and intervening adopted in this review. Each category (e.g., farmer, innovation, and external environment characteristics for extrinsic factors) presents the general value and the partial value divided by country, and that connected to the single level included in the analysis (e.g., for farmer characteristics: socioeconomic characteristics, social networks, personal characteristics, familiarity with technology, status characteristics, and personality characteristics). The table presents both developing and developed country frequencies, allowing for comparative visual interpretation across typologies and categories of variables investigated in the current literature in support of results achieved.

A comparison of the overall frequencies clearly shows that extrinsic factors were the most frequently examined across the selected studies, with a total of 106 assignments. These were predominantly reported in studies conducted in developing countries (n = 79) in line with previous findings about geographical distribution, compared to a lower presence in developed contexts (n = 27). Intrinsic factors follow reflecting the country composition of the dataset with a total score of 91, again with a stronger representation in developing countries (n = 64) than in developed ones (n = 27). Notably, considering the case of developed countries, extrinsic and intrinsic factors were reported with equal frequency (n = 27 each), suggesting a more balanced interest of research between internal and external conditions determinants of individuals in shaping the innovation adoption process in this context, differently from developing countries. Intervening factors, mainly related to communication and support mechanisms, appeared less frequently overall (n = 27), demonstrating a proportion nearly doubled: most references emerged from studies in developing countries (n = 19), with fewer contributions from developed contexts (n = 8).

Within the extrinsic factors group, the most prevalent category is “Farmer characteristics” (n = 43) with a large predominance of studies set in developing countries (n = 34), highlighting the relevance of socioeconomic, personal, and social network characteristics in influencing adoption decisions. The category “Innovation characteristics” is followed by 34 factors (23 developing and 11 developed), indicating widespread attention to technical and functional features, benefits, and implementation costs concerning practices or technology uptake. Though the overall emphasis remained stronger in developing countries, in this case the weight of references’ proportion appears to be reduced compared to the other characteristics, considering the unbalanced representation of developing countries in the dataset. This distribution suggests that innovation specific attributes receive more consistent attention across geographical contexts. Finally, “External environment” was reported for 29 variables (22 developing, 7 developed), underscoring the contextual importance linked to shaping innovation adoption, less significant in developed countries than developing ones.

The intrinsic factors were the second most investigated group in the innovation adoption research field, totaling 91 assignments. As with extrinsic factors, their presence was more pronounced in studies from developing countries than from developed ones. As a result, breakdown of the three categories included in this group reveals that “Attitudes” was the most frequently cited category (n = 48), including 30 mentions from developing countries and 18 from developed ones. This distribution suggests that individual intentions, motivation, subjective norms, and perceived behavioural control play a central role in the adoption process across both contexts. “Perceptions” followed with 34 occurrences, but these were heavily concentrated in developing countries (n = 28 vs. 6), indicating a stronger focus on subjective evaluations and expected outcomes in those settings. This pattern reflects the relevance of individual interpretation processes in contexts where uncertainty, previous experience, and perceived value of innovations shape adoption choices. Finally, “Knowledge” was the least frequently cited category within intrinsic factors (n = 9), with 6 mentions in developing countries and only 3 in developed contexts. Despite its conceptual relevance to the innovation adoption process, variables related to knowledge appeared less frequently in the reviewed studies, making it one of the least represented categories overall.

Concluding the overall findings, the intervening variables referring to communication and support mechanisms were the least cited among the three main typologies, with a total score of 27. As with the other categories, most references originated from studies conducted in developing countries (n = 19), while only 8 were from developed contexts. This internal distribution reflects the emphasis on the role of support and facilitating mechanisms in contexts where structural barriers and informational asymmetries are more prominent. The lower frequency of intervening factors compared to extrinsic and intrinsic variables may be attributable to the fact that these mechanisms are often presented as enabling conditions rather than analyzed as determinants of adoption. Nonetheless, their presence across both settings highlights the importance of facilitation processes that bridge external conditions and internal dispositions in influencing adoption behaviour.

This categorization underscores the complex nature of innovation adoption, emphasizing the interplay between structural conditions, individual dispositions, and support mechanisms, each shaped by their specific context. To enhance the understanding of how these dimensions are represented in the reviewed literature, the following section presents an in-depth analysis of the categories of influencing factors identified across the 57 studies included in this qualitative synthesis. The data reflect the relative frequency with which each variable, identified as influencing innovation adoption, was classified and cited in relative subcategory, providing an overview of their respective weight within the reference base. The internal composition of both groups of intrinsic and extrinsic factors is illustrated in four pie charts (Figure 9), which show the proportional distribution of own subcategories. Among extrinsic factors (Figure 9a–c), socioeconomic characteristics (44%, score of 19) for “Farmer characteristics” stand out as the most frequently studied elements, while “Status characteristics” (2%, score of 1), “Familiarity with technology” and “Personality characteristics” (5%, score of 2 each) were those less investigated in adoption uptake field. In “Innovation characteristics” influencing factors concerning technical and functional aspects of technology or practice examined in each study (41%, score of 14) were the first group for frequency, even if the distribution is fairly balanced. Finally, factors related to political governance (38%, score of 11) and geographical settings (35%, score of 10) in “External environment” category were most emphasized. This distribution of extrinsic variables highlights the relevance of individual conditions and spatial context in agri-food innovation adoption studies. In contrast, the distribution of intrinsic factors, with 93 records distributed across three psychological dimensions (Figure 9d), points to a strong emphasis on psychological and cognitive dimensions, to the disadvantage of “Knowledge” rarely considered. Attitudes account for nearly half of all intrinsic references (53%, score of 48), followed by perceptions (37%, score of 34), while the aspect of knowledge remains markedly underrepresented (10%, score of 9). This imbalance suggests a prevailing research focus on behavioural intentions and subjective evaluations, such as perceived usefulness, ease of use, and risk, rather than on investigating informational or technical training in the literature on innovation adoption.

The following section delves deeper into both categories with thematic interpretations and selected examples from the reviewed literature explaining the extracted factors affecting innovation adoption with a score greater than or equal to 10 (Table 4) because of more representative. Afterwards, with the same rigorous approach was examined how the role of communication and support mechanisms contributed to the decision process in agri-food sector.

Extrinsic Factors Contributing to the Innovation Adoption Process

The internal analysis of “Farmer characteristics” as extrinsic factors revealed 19 cited socioeconomic characteristics influencing farmers’ and stakeholders’ propensity to adopt innovations, with a marked predominance of evidence from developing countries compared to developed ones. Economic resources and financial capacity emerged as the most prominent dimension affecting innovation adoption, with financial constraints representing a critical determinant, particularly in developing country contexts. Access to credit was identified across multiple developing country studies examining different technologies, with Jena and Tanti [45] and Kumar et al. [61] demonstrating its significance for agricultural machinery adoption and improved production practices respectively, while lack of financial resources appeared as a barrier to climate-smart innovations, particularly by smallholder farmers [62]. Income levels showed distinct patterns across geographical contexts, with developing countries emphasizing their influence on farmers’ willingness to pay in circular bio-economy initiatives and to predict farmers’ innovativeness [63,64], while developed nations demonstrated income as an enabling factor for digital technology adoption alongside acreage and demographic variables [33]. According to Alemu et al. [56], the adoption of agricultural innovations was negatively correlated with poorer households and positively correlated with having larger landholdings and households, highlighting economic vulnerability as a fundamental adoption constraint.

Structural characteristics, as farm size, emerged as single or associated determinants to others across both geographical contexts and innovation types, with developing countries showing farm size relevance for green production technologies, ERP systems, and climate-smart innovations [19,34,65], while developed nations emphasized them for innovative contract solutions adoption [66]. Moreover, resource endowments represented a determinant for developing countries, demonstrating to influence farmer-led irrigation [67], conservation agriculture [37,40] adoption, while it enabled diversification strategies across multiple agricultural domains [59]. The integration of farm size with financial resources was particularly evident in developing country contexts, where Gil et al. [68] showed farm size operating alongside financial resources for integrated crop-livestock-forestry systems and in that developed where Drewry et al. [33] investigated digital technology adoption, suggesting generational differences in technological familiarity complement formal educational attainment for advanced agricultural technologies. Educational aspect indeed demonstrated positive associations with innovation adoption across diverse geographical and technological contexts, with developing countries showing farmer education relevance for green production technologies and conservation agriculture [41,65], but it seems not to influence their innovativeness [64], while in developed nations emphasized higher education requirements for innovative contract solutions adoption [66]. The educational dimension appeared particularly pronounced in developed country contexts where advanced educational attainment enabled navigation of sophisticated institutional innovations, contrasting with developing countries where basic educational levels facilitated understanding of sustainable agricultural practices.

The second most crucial aspect in this analysis is “Social network”, revealed 10 belonging to this category influencing farmers’ propensity to adopt innovations, with an unbalanced representation across developing (7 studies) and developed countries (3 studies). Organizational membership, in fact, emerged as the most significant dimension affecting innovation adoption in manner geographically transversal. According to D’Alberto et al. [66], due to the indirect information about the farmer’s network, membership in an organization is a significant factor in determining acceptability. Specifically, cooperative membership form represented an important determinant, demonstrating to be a driver across multiple innovation adoption types, including improved technologies, sustainable management practices and some organizational innovations (e.g., [45,49,61,65]). Hermans et al. [40] state that additionally broader social dynamics, including acceptability and group dynamics, play an important role in the farmer decision-making process influencing conservation agriculture adoption in Malawi [40], while integrated crop-livestock-forestry systems adoption, in the work of Gil et al. [68], demonstrated positive associations with higher social participation and interconnectedness through social networks in Brazil. In contrast, developed country, represented by Europe, where clearly this pattern is very sensitive, organizational membership manifested through different pathways, with membership in organizations serving as important drivers of acceptability for innovative contract solutions through enhanced information networks [66], and direct involvement in agricultural advisory services influencing decision-making processes for digital fertilizer application tools [23]. The collaborative dimension in developed nations is also represented by cooperation among local different actors of value chain in Martens et al. [57], where this innovation enabled the establishment of alternative food networks through local food stores with regional products, suggesting that network based social capital facilitates a sustainable transformation of the regional agri-food system. Gender mediated network influences emerged as a limited characteristic in developing countries, represented by a single study examining wives’ influence on husband-controlled plots affecting drought-tolerant maize adoption [50], highlighting the role of intra-household social dimensions in innovation decision-making processes.

The internal analysis of “Innovation characteristics” as extrinsic factors revealed the most significant factors as belonging to technical and functional aspects linked to own innovation (14 cited factors). These variables influence farmers’ and stakeholders’ propensity to adopt innovations, and they are characterised by a predominant representation from developing countries (9 studies) compared to developed ones (5 studies). Complexity emerged as the most significative dimension affecting innovation adoption, demonstrating negative associations with adoption in developing countries, where low complexity levels facilitated sustainable practices as conservation agriculture and system of rice intensification adoption, while high complexity represented barriers for the uptake of enterprise resource planning technology and a free, government-funded remote-sensing service [34,37,58,69]. This pattern contrasted with developed countries, where complexity manifested through integration and interoperability challenges with existing technologies for smart glasses applications [30], and software system compatibility difficulties for various digital technology adoption [33]. Compatibility and usability characteristics influence developing countries’ farmers, emphasizing how technical compatibility drives farmer decisions in relation to adoption and dis-adoption for different sustainable technology and practices [34,58,69,70]. In contrast, developed nations as Europe and North America, focused on software system integration requirements [30], data utilization difficulties, and speed dissatisfaction for implementation of precision agriculture technologies [33].

Operational requirements and design features represented determinants in developing countries, where adoption was influenced by labour requirements [43], automation capabilities for agriculture 4.0 implementation [25], and modular design features [58], alongside technology unavailability barriers for climate-smart agricultural innovations [62]. In contrast, developed countries emphasized technological advances for sustainable development of food systems [71], commercial orientation for eco-innovation adoption [72], and resource constraints negatively affecting data collection capabilities for precision agriculture applications [32]. These results suggest that innovation characteristics in developed contexts prioritize technological aspects driven by the market, while developing countries focus on operational accessibility as the primary determinant for innovation adoption.

The analysis of factors concerning innovation benefits revealed 11 distinct cited benefits influencing farmers’ propensity to adopt. Relative advantage emerged as the most prominent dimension affecting innovation adoption in developing countries (4 studies), where it facilitated different sustainable practices [34,37,69,70], demonstrating a positive association between them. Economic benefits represented strategic determinants across both geographical contexts, with developing countries emphasizing cash and labor-saving potential for agroforestry innovations [42] and climate-smart agriculture [62], while developed nations focused on farm performance improvements for precision agriculture [28]. Environmental sustainability benefits are geographically transversal, with developing countries highlighting ecological benefits for improved forage technologies [73]. On the other hand, in developed countries, environmental advantages facilitated the adoption of circular technologies, precision agriculture and enabled product differentiation through sustainability labels such as Fairtrade by accessing niche markets for sustainable products, enabling innovation adoption [28,53,71,74].

The internal analysis of External environment characteristics as extrinsic factors identified political governance as a critical dimension affecting innovation adoption, with 11 cited factors across 11 studies. These variables demonstrate how governmental structures, policies, and institutional frameworks influence the decision-making process to adopt a new practice. Also, this category was characterized by an unbalanced representation between developing countries (8 studies) and developed countries (2 studies), with Sharma et al. [21] covering both contexts, comparing in his study four different economies: Asia, Europe, Middle East and North America. Governance frameworks and policies emerged as the most significant dimension, showing how formal governmental structures and policy instruments influence adoption decisions. In developing countries, in fact, local government programs and policies influenced farmers’ preferences for both climate smart agriculture practices [46] and improved forage technologies [73] by regional-level, and agro-environmental regulations influenced green fertilizer technology [70]. While Sharma et al. [21] identified that national policies are the most important enabler of blockchain technology adoption in the agriculture supply chain both across developed and developing context, robust institutional system support through stable agricultural policies is recommend for conservation agriculture promotion [36]. In developed countries, this topic is less explored, with only one study set in North America, where economic and socio-political structural barriers created obstacles by regime level for circular technologies adoption in food systems [71]. Institutional support systems influenced adoption through 3 factors related to direct operational and financial mechanisms. Developing countries demonstrated specific patterns where financial incentives targeted low-income farmers for agriculture 4.0 implementation [25], the ability to get sustainable coffee production certification could support climate smart agriculture adoption [46], and management of irrigation schemes at the institutional level plays a significant role in technology uptake decisions [58]. In contrast, developed countries focused on multi-stakeholder institutional pressure, where non-governmental organization involvement, government training programs, and policy campaigns collectively influenced to adopt sustainability innovations through comprehensive knowledge transfer and incentive mechanisms [53].

The analysis of external environment factors related to geographical settings revealed 10 cited factors across 10 studies that consider it a fundamental dimension affecting the adoption of some typology of innovation. These variables demonstrate how physical environment, spatial characteristics, and location-specific conditions influence to adopt innovations, characterized by a predominant representation from developing countries (8 studies) compared to developed countries (2 studies), both represented by Europe [57,75]. Environmental and climatic conditions constitute the mechanism through which geographical settings influence adoption decisions regarding sustainable management practices and technical and organizational innovations that are linked to the territory, with no involvement of digital tools or precision agriculture. The farmers’ preferences were shaped by the agricultural system profile and agroecological conditions for climate smart agriculture [46], while agro-ecological characteristics for conservation agriculture uptake [40]. Climate-related constraints, including droughts and water restrictions, drove the adoption of irrigation technologies [58], whereas the adoption of improved forage technologies and agricultural machinery was influenced by unsuitable climatic conditions along with exposure to climatic shocks and crop loss [45,73]; in the case of agricultural machinery, adoption decisions were also shaped by plot-specific characteristics [45]. Additionally, resource availability within geographical contexts, particularly labour availability, affected integrated crop-livestock-forestry systems adoption [68]. Land use patterns and spatial positioning reveal how territorial characteristics influence the diversity of contexts in which innovation adoption takes place. In the two European studies, geographic determinants were shown to influence the social innovation capacity of local public and private actors in establishing alternative food networks [57], and a set of dynamic drivers for managing innovation capability was identified relate to the internal and external environment of companies, highlighting how spatial positioning shapes innovation opportunities [75] in the agri-food sector.

Intrinsic Factors Contributing to the Innovation Adoption Process

The analysis of studies focusing on “Attitudes” as intrinsic factors has emerged as a significant component of innovation adoption research, identifying 48 distinct attitudinal factors across 31 studies. These studies recognised attitudinal dimensions as determinants of innovation adoption, representing cognitive orientations and evaluative predispositions that explain the intrinsic decision-making process. More specifically, these variables examine how farmers’ and stakeholders’ cognitive orientations, behavioural predispositions, and value systems influence their propensity to adopt innovations, with representation from both developing countries (18 studies) and developed countries (13 studies). Environmental attitudes and ecological concerns emerged across both geographical contexts. In developing countries, environmental attitudes for a new ecological paradigm drove for example circular bio-economy initiatives [63] and green fertilizer technology adoption [70]. Similarly, developed countries demonstrated environmental orientations through pro-environment attitudes in innovative contract solutions [66], and ecological concerns in favour of novel crop adoption [74] to provide multiple ecosystem services. Environmental corporate culture was identified as a determinant factor in eco-innovation adoption [72], indicating that environmental consciousness operates at both individual and organizational levels. Risk aversion and management represented another significant variable, particularly prominent in developing countries. Risk aversion was explicitly identified as an adoption factor in conservation agriculture contexts [39,40], while risk management capacity and ability to cope with uncertainties and risks showed positive correlation with farmers’ willingness to experiment with new techniques and innovate [59,68]. Trust-related attitudes manifested across both geographical contexts, demonstrating general importance in adoption processes. Bechtet [23] states that trust between European adopters and their advisors was the reason for which they considered adopting digital innovation, serving as a fundamental prerequisite for technology acceptance, while Hermans et al. [40] recognize in the trust in information sources influenced conservation agriculture adoption in Sub-Saharan Africa. However, in the absence of advisors’ offers, considered a crucial prerequisite for adoption farmers by Bechtet [23], farmers do not adopt the tool even if they are well informed about it. Group and subjective norms also demonstrated significant influence on adoption decisions in both country context. Group norms and social pressure were identified as affected factors in different sustainable innovation adoption [53,70,73,76,77] through collective and peer-based mechanisms. Normative beliefs, alongside behavioural and control beliefs, were recognized as determinants in aquafeed innovation adoption [66,76,77], suggesting that social conformity and peer influence constitute important attitudinal dimensions in adoption processes concerning both sustainable and organizational innovations.

Studies focusing on attitudinal factors constituted a substantial component of the reviewed literature, with 34 factors related to perception identified across 21 distinct studies. These papers recognised attitudinal dimensions as determinants of innovation adoption, including cognitive orientations and evaluative predispositions that explain decision-making processes. The geographic distribution demonstrated representation from both developing countries (16 studies) and developed countries (5 studies), showing substantial consistency with the geographic distribution of the dataset.

Technology perception emerged as the most prevalent attitudinal dimension, with perceived usefulness and perceived ease of use documented across multiple innovation contexts and geographic settings. Farmers’ evaluation of digital technologies consistently caught specific perception patterns in developing context: perceived usefulness and ease of use were identified, for example, in microdosing technology [78], and IoT platforms [29]. Similarly, government-funded irrigation scheduling technology in Africa revealed that perceived accuracy, affordability, and particularly easy-to-use computer interfaces providing continuous management advice served as key uptake drivers [58]. In addition, agricultural advice applications demonstrated performance expectancy effects [31], while farmers’ perceptions about technology attributes and effectiveness influenced climate-smart technology adoption [19]. On the other hand, in developed countries, smart glasses technology adoption was affected by perceived benefit and compatibility [30], and precision agriculture contexts revealed value perception by growers and consultants [32].

Risk perception manifested as a significant attitudinal factor across both geographical contexts, in particular concerning consequences and beliefs about climate change effects. Developing countries demonstrated production-focused risk perception influencing climate-smart agriculture technologies in Latin America [49], eco-innovation through better cotton adoption in Asia [79], and drought-tolerant maize adoption in Sub-Saharan Africa [50]. Whereas developed countries showed risk perception through tangible climate change consequences on crops. For instance in Hungary, an exploratory factor analysis revealed that farmers’ perceptions of climate change consequences on agricultural crops significantly influenced adoption of innovative mitigation practices (including new varieties, ice and frost protection, and the use of agro-meteorological data), because of perceived harm from pests, pathogens and illnesses demonstrating substantial impact on new variety adoption, while water damage concerns affected frost protection adoption [44].

Perceived benefits and relative advantage constitute a consistently reported driver of innovation uptake, especially in developing countries. Numerous studies on different typology of technologies and practices [19,70,77,80] conducted in these contexts highlight this factor, while also recording economic benefit skepticism [80] and trade-off evaluations that include intangible social costs [40]. Similarly, in Europe, evaluation of perceived benefits likewise emerged as fundamental for both digital and sustainability innovations. Agricultural stakeholders’ acceptance of smart glasses for on-farm use was directly shaped by the perceived value of the device benefit [30]. In the horticultural sector, adoption of sustainability innovations was motivated by clear advantages such as reduced energy expenditure, compliance with retailer standards, risk mitigation, and opportunities for product differentiation through certification schemes [53]. In the horticultural sector, adoption of sustainability innovations was motivated by clear advantages such as energy cost reduction, compliance with retailer standards, risk mitigation, and opportunities for product differentiation through certification [53]. Benefit evaluation therefore represents a core cognitive process in adoption decisions. In Lao PDR, analysis of four technologies (direct seeding, improved fertilizers, white-rice cultivation for international markets, and cattle vaccination) showed that adoption dropped markedly when additional production costs were not matched by visible profits. While, entrepreneurs, by contrast, judged the same practices less difficult and more affordable when they recognised a positive recompense [81].

Transitioning from utilitarian assessments to constructs value laden, the current literature shows that adoption decisions are embedded in beliefs, values, and social identity. Ecological identity illustrates this evidence, showing a higher propensity of organic farmers in Europe to enroll in value chain and result-based contracts’ adoption, underscoring the role of ecological identity in contractual acceptability [66]. Similarly, cultural orientation and personal innovativeness further shape uptake. Early adopters of integrated crop-livestock-forestry systems exhibit, in fact, higher social participation and openness to change in Latin America [68], while food growers with strong innovative traits are more likely to implement precision agriculture tools in Asia [26]. Therefore, Asian traditionalist farmers reached lower peak adoption rates than entrepreneur-oriented peers when practices involved additional production costs, perhaps without perceiving any benefit [81]. Moreover, trust and advisory relationships prove equally influential in the uptake process. Indeed, in Europe, decision support tools were adopted almost exclusively when promoted by trusted advisors (either a cooperative, the Chamber of Agriculture, or dealers selling the innovation), indicating that trust between farmers and advisors, thus appears to be a crucial lever for digital technologies [23].

Finally, cognitive competencies emerged as particularly underrepresented determinants with nine extracted factors from eight studies, examining this dimension as adoption enabling factor. Of these studies, three examined precision agriculture and alternative food networks in developed country contexts [32,57,82], while five focused on different sustainable innovations in developing countries [34,46,62,68,79]. Technical expertise and understanding constituted an important element both to contribute uptake of sustainable innovation in developing countries [34,68], and especially to guarantee digitalization across developed countries [32,82]. Beyond technical competencies, climate adaptation skills emerge as a specialized constraint within climate-smart agricultural technological innovations, facing implementation barriers related to climate change management skills, and knowledge acquisition and use processes, compounded by limited technology access awareness across developing countries [46,62]. Complementing these findings, Zulfiqar and Thapa [83] confirm that information access provided by formal organizations a favorable impact on the innovation adoption intensity, suggesting that institutional learning modalities influence adoption readiness.

The Role of Communication and Extension as Factors Intervening to Innovation Adoption Process

Communication and extension emerged as critical intervening factors influencing innovation adoption across diverse agricultural contexts, with 27 pieces of evidence from reviewed papers setting both developed and developing countries. The analysis revealed distinct patterns in how communication channels and extension services facilitate technology adoption, with particular emphasis on collaborative approaches and institutional support mechanisms.

Training programs and information dissemination from authoritative sources represented the most frequently cited communication factors, appearing across multiple innovation types and geographical contexts. In developing countries, the formal training programs, indeed, proved essential for eco-innovation and conservation agriculture adoption [20,41,79,83], demonstrating that wider participation of sustainable practices can be positively affecting by institutional support for organizing training programs, providing credit facilities, and delivering information of technologies. Specifically, it emerged the importance to reduce vulnerability through tailored training approaches in developing countries, where tailor-made training programs can be explored to ensure that each gender is targeted via the most appropriate pathway based on its accessibility [19]. In the other hand, in developed countries, intervening factors focused on sophisticated technology requirements to implement digital tools in favour of agricultural innovation systems, where farmers manifested needs for more specialized education and training regarding sophisticated technology [27,32,33]. In this context, the development of collaborative networks supported by institutional partnerships transversely emerged as a fundamental cooperative mechanism, with evidence presenting the evolution from traditional advisory relationships to complex multi-stakeholder platforms. In developed countries, the role of collaboration and its importance is evident when in Europe working in networks (public, private, sectoral or cross-sectoral), connecting different stakeholders as farmers, universities, administrators, and distributors, in order to be able to face the innovation adoption project [28]. This value is evident across advisory organizations (cooperatives, Chambers of Agriculture, trading companies), who serve as network anchors. Indeed, digital innovation adoption is triggered by an offer that farmers directly receive from advisory organizations function in the farming sector as the main advisory suppliers that farmers turn to during the process of adopting the innovation, highlighting the importance of trust relationships within collaborative networks [23]. Moreover, this collaborative approach extended to cooperative systems where cooperation with farmers, consumers, and institutions enabled shared infrastructure, food production and processing methods as well as agricultural know-how [55], demonstrating how new forms of cooperation in relation to the establishment of local food supply can ensure the sustainability of small-scale farming contributing to facilitate both knowledge transfer and resource sharing. In developing countries, the institutional landscape emphasized broader stakeholder integration, with smallholder centred stakeholder collaboration mode and tailored strategies, examining multi-stakeholder platforms better connecting smallholders, scientists, government, and industry [43]. In this context, the collaborative dimension encompassed social network strengthening, with emphasis on strengthening social interactions within the farming community and with other territorial stakeholders and the value chains [59]. Therefore, supporting institutions played fundamental roles within these networks, with promoting institutions facilitating sustainable innovation adoption [20,36,73] and research centres as a source of agricultural information serving as network nodes [50]. In this regard it was demonstrated by Kumar et al. [61] that diversity of information sources influenced adoption patterns, for instance when Asian farmers’ sources of information affected the probability of the adoption of improved practices, with increased adoption when farmers obtained information from informal sources, cooperatives organizations, and public and private extension programs.

4. Discussion

This systematic review identified growing academic interest concerning innovation adoption in agricultural and agrifood sectors worldwide. Research intensity culminated in recent years, reflecting heightened scholarly attention driven by major sustainability policy frameworks such as the European Green Deal, the Farm to Fork and Sustainable Development Goals Strategies. This temporal concentration aligns with global recognition of agricultural innovation’s critical role in addressing climate change and food security challenges [84]. However, it contrasts with earlier periods where innovation research appears more geographically dispersed across country dimensions. The geographical distribution suggests, despite the global relevance of innovation uptake in agrifood, a predominant focus on developing contexts, consistent with substantial funding investments (ranging from $50 to $70 billion per year in 2019 US dollars for the Global South) for agricultural transformation, indicated in the study of Prasad et al. [85]. This concentration reflects the strategic importance of agricultural innovation adoption in regions facing significant sustainability challenges and food security pressures. Contemporary innovation research seems shifted toward contexts facing greater structural constraints and institutional complexities, for instance, focusing on digital transformation in developing countries’ agricultural sectors [86]. This geographical focus provides valuable insights into innovation adoption under resource constraints while highlighting opportunities for comparative research approaches across diverse institutional contexts.

The cross-dimensional analysis of innovations reveals the complexity of innovations investigated from multiple perspectives that extend beyond geographical patterns. This finding confirms Cooper’s [4] theoretical approach regarding the multidimensional nature of innovation adoption. Precision agriculture and decision support technologies emerged as crucial cross-dimensional innovations. These demonstrate how digitalization creates pathways for sustainable agricultural transformation [87]. Contemporary digital innovations facilitate farmer adoption and knowledge diffusion while simultaneously contributing to environmental impact minimization and productivity enhancement. This illustrates the convergence of sustainability and efficiency objectives characteristic of agriculture 4.0 implementation, where innovation serves as a facilitator of digital transformation by addressing behavioral intention to adopt technological solutions that integrate food security awareness with innovation characteristics [86]. Conservation agriculture systems and sustainable management practices dominated multiple ecological categories, particularly soil and water conservation and biodiversity preservation. They demonstrate that productivity and environmental benefits can be achieved simultaneously through integrated approaches. Climate-smart agriculture innovations improved resilience to climate change through integrated adaptation and mitigation strategies while providing empirical evidence for interconnected economic benefits in agricultural systems [88].

Collaboration emerged as a transversal key element in agrifood innovation adoption. It manifests both as an innovation tool (e.g., innovative contract solutions, innovation networks, cooperative systems) and a facilitator for implementing technical and organizational innovations. This finding highlights how collaborative mechanisms enable digital and sustainable transformations while addressing behavioral and cultural dimensions of technology adoption overcoming resistance to change, and bridging the digital divide, as recommended by the European Commission [89]. Collaboration functions not merely as a supportive mechanism but as an integral component of innovation design and implementation. Multi-stakeholder collaboration innovations emphasize the necessity of institutional support for digital transformation [90]. These include blockchain technology, IoT platforms, and enterprise resource planning systems. Blockchain-based frameworks particularly enhance data integrity, trust, and operational efficiency through immutable ledgers and smart contracts while supporting transparency, traceability, and security across food supply chains [91,92]. Organizational innovations represent another crucial collaborative dimension. They include stakeholder interaction models and public-private collaboration frameworks designed to foster sustainable transformation. Innovation network systems ensure fair representation of farmer diversity in innovation processes [93], addressing critiques of top-down innovation approaches that have historically marginalized smallholder perspectives while establishing institutional frameworks that support coordinated technology implementation across value chains.

The framework focused on purposes revealed distinct patterns in research focus across sustainability dimensions. Ecological purpose innovations dominate contemporary literature, demonstrating the sector’s emphasis on environmental sustainability. The emphasis on reducing externalities reflects growing awareness of agriculture’s environmental footprint and the need for mitigation strategies. However, limited attention to biodiversity preservation compared to climate-smart agriculture suggests selective environmental priorities. These may not adequately address the full spectrum of ecological challenges facing agricultural systems.

Economic purpose innovations showed balanced distribution across productivity optimization and economic viability dimensions, in line with the study of Belamkar et al. [94], who explained the dual focus on productivity and yield improvement in agri-food sector. However, minimal attention to rural livelihoods compared to productivity gains suggests that research priorities may not fully align with broader development objectives emphasizing poverty reduction and social equity.

Social purpose innovations demonstrated concentrated focus on farmer adoption and knowledge diffusion. This confirms the central emphasis of included studies while revealing significant gaps in cultural acceptance, gender, and inclusivity considerations. The concentration contrasts with food systems’ transformation approaches that emphasize multidimensional social outcomes as the need for a stronger gender lens [95]. The minimal focus on cultural acceptance and gender dimensions indicates insufficient attention to social equity and inclusivity in innovation processes. This persists despite growing recognition of their importance in sustainable development frameworks and critiques of technology-centred approaches that ignore social differentiation. Contemporary approaches to digital technology introduction in agrifood systems often fail to address key aspects of inclusion, with inclusive innovation mechanisms focusing on beneficiary involvement in innovation processes without elucidating how to operationalize deeper inclusivity that accounts for the nature of engagement and relationship dynamics required for successful outcomes. Institutional purpose represented the least explored dimension. This underscores insufficient integration of governance and institutional aspects in current innovation adoption research [96].

The structured categorization of adoption factors revealed a complexity overview in interactions between structural conditions, individual orientations, and institutional supports in stakeholder decision-making process. This extends previous theoretical frameworks that examined these dimensions in isolation while questioning linear models of technology adoption. Extrinsic factors emerged as most frequently examined, with a predominant focus in developing countries reflecting emphasis on structural and contextual constraints. Intrinsic factors demonstrated notable geographical patterns, with developed countries showing balanced interest between internal and external determinants compared to developing contexts. This distribution might reflect the presence of more stable institutional environments where psychological and cognitive dimensions gain relative prominence compared to contexts characterized by greater external constraints [97], in contrast with the universal applicability of psychological adoption models. The dominance of attitudes within intrinsic factors confirms the central role of psychological determinants across contexts, supporting established theoretical frameworks such as the Theory of Planned Behavior by Ajzen [98]. Environmental attitudes consistently facilitated uptake of sustainability oriented innovations across contexts, supporting theoretical frameworks emphasizing value-belief-norm relationships in pro-environmental behavior [99] while providing empirical evidence for the integration of environmental and economic motivations in agricultural decision-making.

Social networks played differentiated roles across settings. This extends understanding of social influence mechanisms beyond traditional peer effects to include institutional and value chain networks. Cooperative membership emerged as both informational catalyst and mechanism facilitating access to key technologies, as supported by Nakano et al. [100]. This challenges individualistic approaches to adoption while supporting collective action approaches, facilitating information exchange among members, promoting mutual learning and sharing experiences [101]. However, these variables’ influence is frequently mediated by internal factors such as perceived usefulness and risk evaluation. This supports recent theoretical developments emphasizing the interaction between individual decision-making process and external influences in agricultural adoption models [102]. Therefore, finding questions linear diffusion models and supports more complex theoretical frameworks that recognize feedback loops and dynamic interactions between social and individual factors.

Intervening factors, focusing on communication and extension mechanisms, demonstrated predominant concentration in developing countries. This reflects emphasis on support mechanisms in contexts where structural barriers and informational asymmetries are more prominent. The distribution aligns with theoretical frameworks emphasizing the role of extension systems in bridging knowledge gaps and facilitating technology transfer [103]. The relatively lower attention to intervening factors compared to extrinsic and intrinsic variables suggests potential underestimation of their importance in innovation adoption research. Communication and extension factors serve as crucial mediators between innovation characteristics and adoption decisions, facilitating knowledge transfer, capacity building, and institutional coordination. They support theoretical frameworks that emphasize the importance of innovation systems approaches [104,105,106] and provide empirical evidence for the active role of institutional intermediaries in shaping adoption outcomes.

5. Conclusions

This systematic literature review, focusing on various types of innovations in agriculture and food systems, reveals that different innovations work differently. In addition, the adoption success greatly depends on the specific contexts in terms of local culture, geography, and agri-food business organization. For that reason, innovation adoption must be tailored to fit different cases, which are influenced by multiple factors: (i) extrinsic factors such as policies, market conditions, and climate; (ii) intrinsic factors like farmers’ knowledge, attitudes, and resources; and (iii) intervening factors such as training, creating networks, and infrastructure. Moreover, our study underlines that these determinants are interconnected, and collaboration between stakeholders (i.e., farmers, researchers, businesses, and policymakers) is essential for innovation adoption to succeed. In fact, collaboration appears as a tool (i.e., partnerships leading to better solutions) and a facilitator (i.e., networking inducing spread knowledge). Furthermore, policies and innovation programs must adapt to local needs, avoiding generic solutions, learning from worldwide research for customizing approaches for each target area, and allowing for adjustments based on different regions’ challenges, where farmers and local stakeholders have their voice in innovation planning for sustainable and climate-resilient farming.

6. Implications, Limitations and Future Directions

The key findings of this research show that most innovations focus on environmental objectives, but social and institutional aspects (like community needs and governance) are often ignored. In addition, successful adoption of innovations requires addressing three things: external barriers (e.g., costs), internal motivations (e.g., farmer interests), and support systems (e.g., training). In fact, a participatory approach, including all concerned stakeholders and social, ecological, and cultural aspects within a value chain, is essential for innovations to succeed. With respect to the limitations, the research only approached English-language research from two databases, missing some important unpublished or non-English work. Also, we focused our literature review on recent studies (2014–2025), excluding older but maybe relevant references. These choices helped keep the study focused but may have limited the full picture of innovation. From this perspective, future research is needed on how innovations fit into community values and structures. Methods should combine insights from psychology, economics, and governance to better understand adoption challenges.