Shared business models enabling small farmers to afford new technologies

Shared Business Models Enabling Small Farmers to Afford New Technologies TESI DI LAUREA MAGISTRALE IN MANAGEMENT ENGINEERING INGEGNERIA GESTIONALE Author: Joan Oller Corbella Student ID: 10992693 Advisor: Filippo Maria Renga Academic Year: 2024-25 i Abstract This thesis investigates how small farmers can adopt new technologies more effectively through Shared Business Models, a specific type of Sustainable Business Models (SBMs). By focusing on the Unified Theory of Acceptance and Use of Technology (UTAUT) and the Theory of Planned Behaviour (TPB), the study explores how core constructs affect small farmers’ technology adoption processes inside and outside of Shared Model. Additionally, the frameworks are expanded by introducing factors like Price Value, Hedonic Motivation, Habit, Environmental Uncertainty and Sustained Adoption to adapt them to the context of small farmers. A qualitative, multiple-case study approach was chosen to capture the context-specific challenges faced by small farmers, through interviews factors such—limited financial resources, infrastructure gaps, regulatory pressures, and socio-cultural dynamics— are assessed. Interviews and in-depth case analyses reveal that Performance Expectancy, Social Influence, Price Value, Facilitating Conditions are consistently important in shaping Behavioural Intention. Effort Expectancy exerts a minor—but direct—influence on Use Behaviour together with Behavioural Intention and Facilitating Conditions. Sustained Adoption is found to be influenced by Intention and Price Value. Technology familiarity and Geographical Region emerged as key moderating factors in technology adoption decisions. Regarding SBM, Subjective Norms stands out as a negative factor conditioning farmers engagement Intentions. The study then examines whether farmers inclined to adopt technology are necessarily more willing to engage in SBMs, finding no direct correlation between these two preferences. Some participants embrace technology but reject shared models due to concerns about trust or brand confidentiality, whereas others participate in shared initiatives primarily due to market pressures rather than strong technological enthusiasm. To complete this knowledge, a list of drivers and barriers is provided. Overall, the results highlight that individual characteristics and contextual factors— including subsidies, environmental unpredictability, generational succession, and cultural attitudes—jointly determine both technology acceptance and the feasibility of shared models. These findings inform practical recommendations for policymakers, agricultural cooperatives, and technology providers seeking to support smallholders in maintaining both competitiveness and sustainability in an evolving agricultural landscape. Key-words: Shared Business Models, Small Farmers, UTAUT, TPB, Sustainability iii Abstract in italiano Questa tesi esamina come i piccoli agricoltori possano adottare meglio le nuove tecnologie tramite Modelli di Business Condivisi, un tipo di Modelli di Business Sostenibili (SBMs). Basandosi sulla Teoria Unificata di Accettazione e Uso della Tecnologia (UTAUT) e sulla Teoria del Comportamento Pianificato (TPB), lo studio analizza come i costrutti chiave influenzino l’adozione tecnologica dentro e fuori dal Modello Condiviso. Inoltre, amplia questi modelli introducendo Valore del Prezzo, Motivazione Edonica, Abitudine, Incertezza Ambientale e Adozione Sostenuta per adattarli ai piccoli agricoltori. Si è scelto un approccio qualitativo con uno studio multi-caso per capire le sfide specifiche. Attraverso interviste, sono valutati fattori come risorse limitate, carenze infrastrutturali, pressioni normative e dinamiche socio-culturali. Le analisi rivelano che Aspettativa di Prestazione, Influenza Sociale, Valore del Prezzo e Condizioni Facilitanti influenzano costantemente l’Intenzione Comportamentale. L’Aspettativa di Sforzo ha un effetto minore, ma diretto, sul Comportamento d’Uso, insieme a Intenzione e Condizioni Facilitanti. L’Adozione Sostenuta dipende da Intenzione e Valore del Prezzo. Familiarità con la tecnologia e Regione Geografica emergono come fattori moderatori chiave. Per quanto riguarda gli SBM, le Norme Soggettive si distinguono come un fattore negativo che condiziona le intenzioni di coinvolgimento degli agricoltori. Lo studio analizza poi se chi adotta tecnologia sia più propenso ai SBMs, senza trovare una correlazione diretta. Alcuni usano tecnologia ma rifiutano i modelli condivisi per mancanza di fiducia o riservatezza, mentre altri vi partecipano per pressioni di mercato, non per entusiasmo tecnologico. Una lista di fattori trainanti e ostacoli completa l’analisi. In sintesi, i risultati mostrano che caratteristiche personali e fattori contestuali, come sussidi, incertezza ambientale, successione generazionale e cultura, determinano sia l’accettazione tecnologica che la fattibilità dei modelli condivisi. Questi risultati offrono suggerimenti pratici a politici, cooperative agricole e fornitori tecnologici per aiutare i piccoli agricoltori a restare competitivi e sostenibili in un settore in evoluzione. Parole chiave: Modelli di Business Condivisi, Piccoli agricoltori, UTAUT, TPB, Sostenibilità. v Contents Abstract ................................................................................................................................. i Abstract in italiano .......................................................................................................... iii Contents ............................................................................................................................... v Introduction, Objectives and Structure ......................................................................... 1 1 Research Context ...................................................................................................... 3 1.1. Agricultural Sector Context ......................................................................... 3 1.2. Sustainable Business Models ....................................................................... 5 2 Literature Review ..................................................................................................... 9 2.1. Sustainable Business Models in Agriculture ............................................. 9 2.2. Theoretical Frameworks for Technology Adoption & Use Behaviour 15 2.3. Description of Which Parts of the Theories Are Already Researched in the Agricultural Sector ................................................................................................ 23 3 Methodology ........................................................................................................... 31 3.1. Research Approach ..................................................................................... 31 3.2. Frameworks Utilized .................................................................................. 32 3.3. Sample Description ..................................................................................... 32 3.4. Ethical Considerations ................................................................................ 33 3.5. Data Collection ............................................................................................ 33 3.6. Data Presentation ........................................................................................ 34 3.7. Data Analysis ............................................................................................... 36 4 Frameworks ............................................................................................................. 41 4.1. Reasons for using UTAUT and TPB ......................................................... 41 4.2. Customizing the UTAUT Model for Small Farmers' Technology Adoption ....................................................................................................................... 42 4.3. UTAUT and TPB as Analytical Tools ....................................................... 45 5 Data Presentation ................................................................................................... 49 5.1. Gabri .............................................................................................................. 50 5.2. Jordi ............................................................................................................... 53 5.3. Berta .............................................................................................................. 56 vi | Contents 5.4. Sancho ........................................................................................................... 59 5.5. Joan ................................................................................................................ 62 6 Results and Discussion ......................................................................................... 67 6.1. Within-Case Analysis ................................................................................. 67 6.2. Cross-Case Analysis .................................................................................... 90 6.3. Results Summary ....................................................................................... 101 6.4. Discussion ................................................................................................... 103 7 Conclusions and Recommendations ................................................................ 109 Bibliography ................................................................................................................... 111 A Appendix: Interviews template ......................................................................... 119 List of Figures ................................................................................................................. 127 List of Tables .................................................................................................................. 129 Acknowledgments ......................................................................................................... 131 1 Introduction, Objectives and Structure Small-scale farming is increasingly squeezed by economic pressures, environmental uncertainties, and technological advancements that often seem more suited to larger organizations. Despite their vital role in rural development, biodiversity, and local markets, small farmers face limited access to financial capital, modern machinery, and digital tools. As a result, they frequently risk losing competitiveness or even exiting the agricultural sector altogether. In response to these challenges, this thesis examines how shared business models—a subset of sustainable business models (SBMs)—can enable small farmers to collectively afford new technologies that might otherwise remain out of reach. Specifically, the project focuses on two frameworks for analysing technology adoption and collective behaviours: the Unified Theory of Acceptance and Use of Technology (UTAUT) and the Theory of Planned Behaviour (TPB). These models offer complementary perspectives on individual decision-making (UTAUT) and grouprelated factors (TPB), capturing how different factors influence small farmers adopt innovation or engage in collaborative arrangements. The key objectives of this thesis are: • To identify the main drivers and barriers that influence small farmers' adoption of new technologies. • To examine the challenges and enabling factors that affect small farmers' engagement in shared business models. • By interacting the previous two points, to identify the drivers and barriers that emerge when adopting an innovation through a shared model. • To explore whether farmers who are more inclined to adopt innovations are also more willing to participate in shared business models. To achieve these objectives, the thesis is structured to first provide contextual and theoretical foundations (Chapters 1 and 2), followed by methodology and framework development (Chapters 3 and 4). It then presents case study data (Chapters 5) and the analysis together with results (Chapters 6), leading to conclusions and practical recommendations (Chapter 7). This structure ensures a clear progression from understanding the problem to proposing actionable solutions. 9 2 Literature Review This chapter explores the key theories and research that explain how farmers adopt new technologies and how these decisions connect to sustainable business models in agriculture. Understanding the factors that influence technology adoption and the expected behaviour in front of a new business model is essential. This thesis is focusing on shared models, which are interesting to improve accessibility and efficiency in farming. The discussion begins with an examination of sustainable business models in agriculture and their potential to address financial and operational challenges faced by small farmers, with a special focus on the shared models. The focus then shifts to theoretical frameworks that provide insight into technology adoption and behaviour, particularly UTAUT and TPB, which highlight the role of different factors in decisionmaking. Building on these foundations, the relationship between technology adoption theories and sustainable business strategies is explored, emphasizing how shared models fit within existing research on innovation diffusion. Finally, previous studies in the agricultural sector are reviewed to assess what has already been investigated and where gaps remain. This approach provides a strong theoretical basis for analysing how shared business models could support small farmers in adopting new technologies. 2.1. Sustainable Business Models in Agriculture Sustainable business models (SBMs) in agriculture aim to balance economic viability with environmental stewardship and social responsibility. As agriculture is both a significant economic sector and a key contributor to global sustainability challenges, adopting sustainable business models has become imperative. These models seek to integrate resource efficiency, resilience to climate change, and equitable value distribution while ensuring long-term productivity and food security (Sengupta et al. 2024; Sadovska, Fernqvist, and Barth 2023). The transformation of business models in the agricultural sector is being driven by multiple factors, including regulatory pressures, evolving consumer preferences, and advances in technology. While large agribusinesses are at the forefront of adopting sustainable practices, small and medium-sized farms face greater constraints in 10 2| Literature Review integrating these models due to limited access to capital, technology, and knowledge networks (Donner and De Vries 2023). This disparity is generating greater differences between larger farms, which can more easily invest in sustainability-driven innovations, and small-scale farmers, who struggle to access the necessary resources and infrastructure. As a result, understanding the barriers that small farmers face in adopting sustainable business models and accessing technology is essential to ensuring a more inclusive and equitable transition towards sustainability in agriculture. Agriculture has witnessed the emergence of several sustainable business models, each targeting different aspects of sustainability and efficiency. These models include: • Circular Economy Models: These emphasize waste reduction and resource efficiency by repurposing agricultural by-products, promoting soil regeneration, and adopting closed-loop production systems (Donner and De Vries 2023). Techniques such as composting, bioenergy production, and regenerative agriculture fall under this category. • Traceability-Based Models: By leveraging digital tools such as blockchain and IoT, traceability models enhance transparency in food production, ensuring compliance with sustainability standards and consumer demand for ethically produced goods (Sengupta et al. 2024). • Frugal Innovation Models: These models focus on cost-effective, simplified technologies tailored to small-scale farmers who cannot afford expensive machinery or digital solutions. Low-cost irrigation systems or bio-fertilizers exemplify frugal innovation in agriculture (Sadovska, Fernqvist, and Barth 2023). • Design for Durability, Reusability, and Recyclability: Some sustainable business models focus on extending the lifespan of agricultural tools, machinery, and packaging materials. This principle ensures lower material consumption and minimizes waste generation. • Closed-Loop Supply Chains: This model extends beyond circular economy principles by ensuring that every component of agricultural production, from inputs to outputs, remains within a controlled cycle, reducing waste and maximizing efficiency. (Gholian-Jouybari et al. 2023) • Co-Creation Models: These models involve collaboration between farmers, researchers, and consumers to develop innovative agricultural solutions. Farmers participate in the design and implementation of new practices, ensuring that sustainability innovations align with real-world needs. • Shared Business Models: Shared economy approaches in agriculture involve collaborative ownership of resources such as machinery, storage facilities, and processing units, allowing small farmers to access essential technology without 2| Literature Review 11 significant financial burdens. This model will be further explored in the next section. 2.1.1. Shared models (Sharing economy) Karel Alloh defines the sharing economy as a business model that uses information technology and marketing to facilitate the exchange of goods and services between individuals (Wirtz et al. 2019; Mody et al. 2023). These models are typically mediated by digital platforms or community-based networks, enabling participants to optimize resource utilization while reducing costs and environmental impact (Guyader and Piscicelli 2019). The sharing economy fosters a culture of access over ownership, encouraging more efficient and sustainable use of resources (Pouri and Hilty 2018; Hong and Yoo 2020). Its sustainability includes environmental, social, and economic aspects—with environmental benefits linked to higher resource efficiency and energy savings (Laukkanen and Tura 2020), social benefits focusing on well-being and innovation (Martin, Upham, and Budd 2015; Lan et al. 2017), and economic advantages tied to cost-effectiveness and broader participation (Curtis and Mont 2020; Colapinto et al. 2020; Curtis and Lehner 2019). While the sharing economy has primarily emerged in urban settings, its application in agriculture is expanding, offering small farmers new opportunities to collaborate, pool resources, and collectively access critical assets such as machinery, land, storage facilities, and knowledge-sharing platforms. 2.1.2. The Role of Shared Models in Agriculture Shared models in agriculture are particularly valuable for addressing resource inefficiencies and enabling collective access to key agricultural assets. For example, in regions where land fragmentation limits productivity, farmers can benefit from land pooling initiatives, where multiple small plots are consolidated for joint cultivation. Similarly, cooperative irrigation systems allow multiple farmers to share water resources, reducing waste and ensuring more sustainable usage (Miralles, Dentoni, and Pascucci 2017). These approaches lower financial barriers, improve operational efficiency, and enhance resilience against economic and environmental uncertainties (Miralles, Dentoni, and Pascucci 2017). Beyond resource efficiency, shared models also facilitate entry into high-cost technological investments. Precision farming tools, such as drones for crop monitoring or automated harvesting systems, remain inaccessible for many smallholders due to high costs. However, through shared ownership agreements, farmers can co-finance, jointly use, and maintain such technologies, making innovation adoption more feasible (G. Artz and Naeve 2016). Successful cases include machinery cooperatives in Europe, where groups of farmers collectively purchase and maintain specialized equipment, lowering costs and improving machinery utilization rates. 12 2| Literature Review However, the success of these models depends on effective governance mechanisms and social trust. While some shared models operate informally among trusted community members, others require structured agreements, membership fees, and formalized scheduling systems to ensure fair access and maintenance responsibilities. The level of formality often depends on the size of the cooperative and the complexity of the shared asset. 2.1.3. Types of Shared Models in Agriculture While various shared models exist, this thesis will focus equally on machinery sharing and agricultural cooperatives, as both represent structured and impactful strategies for helping small farmers overcome barriers. Machinery sharing is particularly relevant due to its role in reducing costs and increasing access to technology, while cooperatives are widely used and provide organizational and financial support. Other forms of sharing models will be briefly introduced for context. 2.1.3.1. Machinery Sharing Machinery sharing enables farmers to access and use agricultural equipment without full ownership, reducing investment costs and increasing efficiency. This is particularly useful for high-cost seasonal machinery, such as harvesters, irrigation systems and precision planting tools. There are three main types of machinery sharing: • Informal Machinery Sharing: In this model, ownership remains individual, meaning that each piece of equipment is owned by a specific farmer rather than a group or organization. Farmers privately arrange to lend or share equipment with others in their community, typically through mutual agreements based on trust rather than formal contracts. Usage is often negotiated case by case, with farmers either offering machinery in exchange for labour, shared maintenance, or reciprocal use of other equipment. This model is common in tight-knit farming communities, where farmers help each other manage seasonal demands without significant financial investment (G. Artz and Naeve 2016). • Machinery rings: Machinery rings are structured networks where farmers can rent or borrow agricultural equipment from a shared pool instead of purchasing it individually. These systems are managed by an organization, association, or third party, which oversees scheduling, maintenance, and cost distribution. In this model, the machinery is typically owned by the ring itself, a third party, or individual members who rent out their equipment for temporary use. Farmers pay a membership fee or per-use rental fee to access the equipment as needed, allowing them to use specialized machinery without the financial burden of ownership. This model is particularly common in 2| Literature Review 13 Germany and the UK, where it allows small farmers to access specialized machinery as needed (Alpaslan Başarık 2015). • Machinery Cooperatives: Are long-term collaborative models where farmers co-own and co-manage machinery, making joint financial contributions toward purchasing, maintaining, and using equipment. Machinery cooperatives require members to invest in ownership, meaning they collectively purchase and manage farm machinery under agreed-upon terms. Farmers in a cooperative set usage schedules, cost-sharing rules, and maintenance responsibilities to ensure fair access and long-term sustainability. This model allows for greater control over the machinery, as decisions are made democratically by cooperative members. An example is the Coopérative d’Utilisation de Matériel Agricole (CUMA) in France, where farmers collectively purchase and maintain equipment, reducing operational costs. 2.1.3.2. Agricultural Cooperatives Agricultural cooperatives involve collective ownership and management of farming resources, allowing farmers to reduce costs, improve market access, and share production risks. While this thesis will not focus on cooperatives in general, machinery-sharing cooperatives have already been explained in the previous section. Here, its introduced other types of agricultural cooperatives: • Input and Purchasing Cooperatives: Farmers buy seeds, fertilizers, and other inputs in bulk, lowering costs and improving supply chain stability. By purchasing collectively, farmers benefit from economies of scale, making essential farming inputs more affordable and accessible. • Storage and Processing Cooperatives: These cooperatives provide shared postharvest storage and processing facilities, allowing farmers to reduce postharvest losses, improve product quality, and access better market opportunities. By working together, farmers can negotiate better prices and expand into new markets. Cooperatives play a significant role particularly in input management and postharvest processing. 2.1.3.3. Other Shared Models • Land Sharing: Farmers collaborate on land use and management to optimize efficiency and productivity. This can involve joint cultivation agreements, collective land leasing, or cooperative land ownership. In some regions, land leasing cooperatives allow smallholders to combine fragmented plots for shared farming, enabling more efficient resource use and improved yields. 14 2| Literature Review • Knowledge-Sharing Networks: Digital platforms and farmer organizations facilitate training, technical knowledge exchange, and best-practice dissemination, improving innovation and sustainability in farming. • Community-Supported Agriculture (CSA): Consumers pre-purchase farm produce, ensuring stable income for farmers while promoting direct relationships between farmers and consumers (Miralles, Dentoni, and Pascucci 2017). • Peer-to-Peer Marketplaces: Online platforms allow farmers to rent equipment, trade agricultural inputs, or share labour, increasing flexibility and improving access to necessary resources (Curtis and Mont 2020). 2.1.4. Drivers of Shared Models in Agriculture The adoption of shared models in agriculture usually is influenced by: • Cost Reduction and Efficiency: Farmers can reduce financial strain by sharing the costs of machinery, irrigation systems, and post-harvest processing units. Studies indicate that machinery cooperatives can lower equipment costs by up to 30–35% compared to individual ownership (Harris and Fulton 2000). • Access to Advanced Technology: Shared investment in high-cost agricultural technology, such as drones, automated harvesting systems, and soil analysis tools, enables small farmers to compete with large agribusinesses (G. M. Artz, Colson, and Ginder 2010). • Risk Mitigation: By distributing financial and operational risks across multiple participants, shared models enhance farm resilience against market volatility and climate-related disruptions (De Toro and Hansson 2004). • Knowledge and Skill Sharing: Collaborative farming models facilitate peer learning, allowing farmers to exchange expertise, adopt best practices, and improve efficiency (Schanes and Stagl 2019). • Sustainability and Circular Economy: Shared models promote resource efficiency by reducing redundant investments, encouraging regenerative agricultural practices, and minimizing input waste (Curtis and Mont 2020). 2.1.5. Barriers to Adoption of Shared Models Despite the potential benefits, shared agricultural models face significant barriers: 1. Coordination and Governance Challenges: Establishing effective decisionmaking structures within cooperative models can be difficult. Conflicts over resource allocation, scheduling, and financial contributions often arise(Wolfley et al. 2011). 2| Literature Review 15 2. Trust and Social Dynamics: The success of shared models depends on trust among participants. Disputes over equipment maintenance responsibilities and usage scheduling are common in machinery-sharing initiatives (G. Artz and Naeve 2016). 3. Legal and Regulatory Constraints: The lack of clear legal frameworks for shared ownership agreements and cooperative management complicates implementation, particularly in regions with rigid agricultural policies (Long and Kenkel 2007). 4. Infrastructure and Logistics Issues: Geographical dispersion of farms can hinder the efficiency of shared machinery and storage facilities, increasing transportation costs and logistical complexities (Miralles, Dentoni, and Pascucci 2017). 5. Farmers social and Cultural Resistance: Convincing farmers to adopt shared models can be challenging due to deeply ingrained traditional practices and a preference for individual operations. This resistance is often rooted in a lack of awareness or understanding of the benefits associated with shared models, as well as concerns about the complexities of collaboration (G. Artz and Naeve 2016). Since these cultural and behavioural factors, along with other adoption barriers, play a crucial role in the feasibility of shared models in small farmers, this thesis aims to investigate them further. Therefore, 2.2. Theoretical Frameworks for Technology Adoption and Use Behaviour introduces the basic concepts that the investigation will use to provide a deeper understanding of the factors influencing farmers' decision-making and willingness to adopt shared business models. After analysing the drivers and barriers farmers face in this context, the findings will be compared with those from the literature review to complement, confirm, or challenge them. 2.2. Theoretical Frameworks for Technology Adoption and Use Behaviour In today's world, where technology is advancing rapidly and new applications emerge daily, understanding technology acceptance has become a crucial area of interest. For decades, social science models and theories have sought to explain how and why users adopt new technologies. One of the earliest and most recognized theories is the Innovation Diffusion Theory (IDT) by Rogers (1962), followed by the Technology Acceptance Model (TAM) proposed by Davis in 1989 es (Rogers, Singhal, and Quinlan 2014) and (Davis 1989). TAM emphasizes two key factors in technology adoption: perceived usefulness and perceived ease of use. These two elements remain central in more recent theories and 16 2| Literature Review are fundamental to understanding technology acceptance. TAM is widely regarded as a concise and straightforward model for evaluating user adoption, see Figure 2.1. Figure 2.1: Illustration of the Technology Acceptance Model (TAM). Source: (Miller and Khera 2010) Building upon these concepts, a more comprehensive framework was introduced in 2003: the Unified Theory of Acceptance and Use of Technology (UTAUT) by Venkatesh et al. from 2003 (Venkatesh et al. 2003). UTAUT extends TAM by incorporating additional variables that influence adoption behaviour. Another widely used model is the Theory of Planned Behaviour (TPB), developed by Ajzen in 1991 (Ajzen 1991), which provides a broader framework for predicting human behaviour beyond technology acceptance. Both UTAUT and TPB are the primary models considered in this literature review, as they offer a more comprehensive perspective on behavioural dynamics compared to TAM and IDT. These models have been selected over their predecessors because they better align with the focus of this thesis: understanding the acceptance of shared business models that enable small farmers to adopt new technologies. Additionally, UTAUT already integrates elements of both TAM and IDT, making it a unified framework that encapsulates previous research in this field. 2.2.1. The Unified Theory of Acceptance and Use of Technology (UTAUT) 2.2.1.1. The original UTAUT Model By the early 2000s, substantial evidence and a wide range of theories had been developed to explain user behaviour in relation to technology adoption. These theories originated from various disciplines, which often limited their applicability in specific contexts. As a result, there was a growing need for a unified approach that could integrate the diverse variables and perspectives from these disciplines, creating a single, comprehensive theory applicable across different scenarios. To address this, a literature review was conducted to identify similarities and differences among existing technology acceptance models within three main research streams: behavioural 2| Literature Review 17 psychology, social psychology, and information system management (Papagiannidis 2022). Venkatesh, who led this research effort, identified several limitations in the existing theories. One key issue was the lack of empirical testing and direct comparisons between different acceptance models, which left room for speculation regarding the predictive power of each theory's constructs. Additionally, methodological inconsistencies and variations were detected across studies. Another major limitation was that most prior research assumed technology acceptance occurred in voluntary contexts, making it difficult to generalize findings to settings where technology adoption might be influenced by external factors. The empirical comparison of these theories ultimately led to the development of the Unified Theory of Acceptance and Use of Technology (UTAUT). This model, introduced by Venkatesh and his collaborators in their work User Acceptance of Information Technology: Toward a Unified View, proposes that technology usage is determined by behavioural intentions, which, in turn, are shaped by four main constructs: 1. Performance expectancy 2. Effort expectancy 3. Social influence 4. Facilitating conditions The first three constructs directly influence usage intention and behaviour, while the fourth construct directly affects user behaviour. Additionally, factors such as gender, age, experience, and voluntariness of use serve as moderators, affecting the strength of these relationships. See Figure 2.2 and Table 2.1 for a better understanding. 24 2| Literature Review intention, technology use and moderators. Additionally, it includes factors that will be introduced in later chapters, but are already examined here to ensure that, when comparing the thesis findings with existing research, all relevant information is consolidated in one place. This approach facilitates the alignment with the methodology outlined in 3. Methodology. For this reason, the review also covers price value, hedonic motivation, habit, environmental uncertainty, sustained adoption, and other moderators, providing a comprehensive understanding of their role in agricultural technology adoption. While some of these constructs, such as performance expectancy and social influence, are well-established predictors, others, such as hedonic motivation and habit, remain underexplored in the farming context: 1. Performance Expectancy Existing research on farm technology adoption consistently highlights performance expectancy as a strong predictor of whether farmers perceive a new tool to bring tangible benefits. Several sources, including the (Handoko Putra et al. 2023) confirm that if farmers believe it will raise efficiency or lower labour costs, their willingness to adopt goes up. When dealing with smallholders specifically, demonstrating gains such as yield increases, marketing advantages, or the capacity to compete with large agribusinesses heightens confidence in adoption (Triandini et al. 2023). Another example is the discussion by (G. M. Artz, Colson, and Ginder 2010), where small operators saw new technology more favourably if it directly enabled them to remain competitive. Conversely, some authors note that if the expected performance improvements are uncertain, caution arises. Studies specifically referencing advanced machinery-sharing find that, although sharing can reduce purchase costs, some farmers doubt whether the resulting performance improvements (like timeliness or better yields) will be large enough to offset complexities (Wolfley et al. 2011). Overall, performance expectancy remains one of the most consistently documented motivators for farm technology acceptance. It is thus fair to conclude that performance expectancy is consistently one of the most vital motivators for acceptance decisions, as farmers highly value whether a new technology can concretely boost their production or reduce costs. 2. Effort Expectancy Many investigations also address the perceived ease of learning or implementing a given agricultural technology, commonly reported as a driver of acceptance (Handoko Putra et al. 2023). The typical finding is that if farmers view a digital platform or advanced machinery as difficult to operate or maintain, their acceptance intention decreases. Some case studies on farmers sharing specialized equipment (Alpaslan Başarık 2015; De Toro and Hansson 2004) point out that compatibility with existing farm routines significantly influences perceived effort. If farmers require extensive training, they may fear disruptions or time investment, becoming reluctant to adopt. 2| Literature Review 25 So far, literature confirms that simpler, more user-friendly technologies facilitate acceptance, especially where extension services provide direct coaching. 3. Social Influence The literature on advanced agricultural technology often highlights the impact of social influence, which functions similarly to local norms or community approval. On (G. Artz and Naeve 2016) note that peer endorsement or neighbour success stories encourage adoption. When community networks or cooperative leaders advocate the new technology, farmers perceive it as more legitimate. In West African contexts, for instance, smallholder adoption soared when local champion farmers publicly endorsed technology or method changes (Pierrette, Coulibaly et al. 2021). The consensus is that social influence strongly drives acceptance, though it can also hamper it if negative local gossip or a preference for individualistic farming is widespread. There are no contradiction about the effect of social, but some do note that social influence alone may not be sufficient if the perceived performance advantage is missing. 4. Facilitating Conditions A major theme in acceptance studies is whether farmers have the resources (both financial and infrastructural) and institutional support they need to adopt. Several documents (Wolfley et al. 2011; Long and Kenkel 2007) depict that well-defined contractual frameworks and extension support can ease farmers’ readiness to share or adopt new machinery. Others, like (De Toro and Hansson 2004), highlight that a lack of clarity in scheduling or cost-splitting dampens acceptance. Among smallholder communities, if facilitating conditions (like training programs or local maintenance services) are absent, perceived difficulty remains high. The literatures reviewed almost unanimously confirm the role of facilitating conditions as a strong acceptance factor in agricultural technology. 5. Price Value Price value refers to whether farmers perceive that a technology’s financial returns justify its overall expense. While many studies discuss costs tangentially, only a few explicitly treat “price value” as a standalone factor. One that does is (Antwi-Boampong et al. 2024), which analyses IoT adoption among smallholder farmers, concluding that their willingness to adopt depends heavily on whether near-term economic benefits outweigh the initial capital outlay. This resonates with broader findings on costsharing, (Harris and Fulton 2000) suggest that if farmers see a favourable ratio of benefit to total cost, acceptance is likely to rise. Smallholders under tight capital constraints, as further noted in (Handoko Putra et al. 2023), pay even closer attention to price. Conversely, if a tool is too expensive or if cost-splitting becomes complicated (Miralles, Dentoni, and Pascucci 2017), acceptance drops. 26 2| Literature Review Similarly, (Faridi, Kavoosi-Kalashami, and Bilali 2020) argue that financial constraints are a major barrier to adoption, making perceived value a critical determinant. Farmers assess not just direct costs but whether expected benefits justify the investment, shaping their adoption decisions. Though the principle is widely acknowledged, most research references price considerations implicitly, without labelling “price value” as a distinct dimension. Thus, while the economic ratio of gains to costs is undeniably important to farmers, few investigations except for those like (Antwi-Boampong et al. 2024) offer a direct, indepth treatment of this construct in the context of technology uptake among smallscale producers. 6. Hedonic Motivation References to hedonic motivation, meaning the enjoyment or pleasure derived from using new agricultural technology, are relatively sparse. Researchers studying machinery adoption or cost-sharing models typically stress pragmatic (Pongsuwan 2019) factors rather than the “fun” or “entertainment” value of the innovation. In (Antwi-Boampong et al. 2024), the discussion on smallholder farmers’ technology uptake does touch upon user-friendly features but frames them as practical rather than hedonic. Some mention of “convenience” appears in (Handoko Putra et al. 2023); however, no findings elevate the enjoyment dimension to a primary driver for acceptance. Consequently, it is assumable that hedonic motivation is not a widely studied or highly influential factor in farmers’ decision-making on new technology, especially among small operators who prioritize economic and labour-saving benefits. 7. Habit Habit usually is not treated as a discrete factor, but the idea of whether a new technology “fits” one’s existing farm routine, rather than causing major disruption, does surface in some studies. For example, in (De Toro and Hansson 2004), several farmers expressed reluctance if advanced machinery required re-timing certain operations or forced them to adjust field schedules drastically. They questioned how easily the innovation would “slot in” to daily tasks without introducing conflict. Another example is (Triandini et al. 2023), which implies that when digital tools harmonize naturally with a farmer’s established practices (e.g., record-keeping or field monitoring), acceptance is smoother because it feels like an incremental improvement rather than a sharp change. The consistent thread is that if adopting a technology seems to blend into existing routines with minimal upheaval, farmers perceive fewer risks and are likelier to embrace it. Conversely, a sense that one’s normal routine can change drastically can block their readiness to adopt. Additionally, (Handoko Putra et al. 2023) highlight that long-term adoption is not solely based on initial intention but also on the formation of habitual use. This suggests that technologies seamlessly integrated into existing routines are more likely to be retained over time, as farmers develop automatic behaviours in using them. The 2| Literature Review 27 ability of a technology to become an ingrained part of daily farming practices thus plays a crucial role in ensuring its sustained adoption. So, habit is not typically considered as a main construct in studies but is extendedly accepted that harmonizing a new solution with farmers’ established routines emerges as a complementary factor of technology acceptance in agriculture. 8. Environmental Uncertainty Environmental uncertainty has been recognized as a key factor influencing small farmers' adoption of digital platforms (Cimino et al. 2024). Rapid technological advancements and market fluctuations increase uncertainty, prompting farmers to seek solutions that enhance adaptability and resilience. The perceived ability of a technology to help navigate such uncertainties can significantly impact adoption decisions. A few references (De Toro and Hansson 2004) also indicate that unpredictable weather or shifting market conditions complicate technology scheduling and cooperative machinery usage. These authors highlight how uncertain harvest windows can deter farmers from trusting advanced equipment-sharing arrangements if the risk of delayed access to machinery is perceived as too great. However, since "environmental uncertainty" is not systematically used as a UTAUT construct, there is limited reliable information to confirm that the way a technology interacts with environmental uncertainty is a key factor. 9. Behavioural Intention, Technology Use and Sustained Adoption In UTAUT studies, behavioural intention typically serves as the main dependent variable, reflecting farmers’ readiness to adopt. Most references confirm that a solid intention (driven by perceived performance advantage, effort expectancy, social influence, and facilitating conditions) usually culminates in actual usage. Introducing the idea of this thesis to combine technology adoption with a shared model, not only the use should be considered. G. Artz and Naeve (2016) warn that disagreements over scheduling or contract terms can disrupt ongoing adoption, implying that sustained usage may require stable collaborative governance. Very few studies delve deeply into “long-term usage” metrics; they mainly track initial acceptance. In short, the basic acceptance pattern, intention predicting usage, remains validated, but detailed exploration of how that usage remains stable over time is less common in the smallfarm context. 10. Moderators: Technology Familiarity, Gender, Age, Experience, Voluntariness of Use Several demographic and contextual factors moderate the adoption of new agricultural technologies among smallholder farmers. Age and experience have been widely studied, with findings suggesting that while age itself does not significantly alter adoption behaviours, prior experience with agricultural technology does. For instance, (Zhang et al. 2024) found that farmers with more years of technology use 28 2| Literature Review were more likely to perceive higher performance benefits and cost-effectiveness, leading to stronger adoption intentions. Gender differences seem to play a crucial role, studies show that women farmers in many regions have lower adoption rates due to socio-economic constraints, lack of technical training, and traditional labour divisions (Radović-Marković, Kabir, and Jovičić 2020). Technology familiarity is another critical moderator; (Gabriel and Gandorfer 2023) observed that small-scale European farmers tend to adopt sequentially, with early exposure to digital tools increasing the likelihood of adopting additional technologies over time. Similarly, (Handoko Putra et al. 2023) highlight that farmers who have previously interacted with related technologies may perceive adoption as easier and be less resistant to change, as familiarity reduces perceived effort and uncertainty. Voluntariness of use, although less frequently examined, remains relevant, as research indicates that farmers who perceive technological adoption as a necessity rather than a choice, due to policy incentives or market pressures, may exhibit different behavioural patterns (Fadeyi, Ariyawardana, and Aziz 2022). Despite recognition of these moderators, existing studies often fail to comprehensively analyse their combined effects, leaving a gap in the literature regarding how demographic, social and contextual factors collectively shape smallholder farmers' technology adoption. 2.3.2. TPB broken down Focusing on the other theory discussed, this section reviews existing research on TPB’s main dimensions: attitude toward behaviour, subjective norms, and perceived behavioural control, along with their subcomponents, to understand their role in the adoption of shared business models in agriculture. The rationale for incorporating TPB alongside UTAUT is further explained in 4.1.Reasons for using UTAUT and TPB , with some initial insights already provided in their respective introductions. Existing research suggests that economic benefits, peer influence and a sense of control over shared resources strongly impact farmers’ participation in such models. However, challenges such as coordination difficulties, power imbalances and scepticism about fairness can act as barriers to adoption. By breaking down these elements, this section highlights how TPB can help explain why some farmers embrace collaborative approaches while others hesitate. 1. Attitude toward Behaviour (including Evaluations of Behavioural Outcomes) Attitude toward the behaviour reflects whether farmers have a favourable or unfavourable appraisal of adopting machinery-sharing or a cooperative business model. In agriculture, these attitudes are often shaped by how well farmers think such a model can help them increase yields, lower costs, or otherwise benefit (De Toro and Hansson 2004; Wolfley et al. 2011). When farmers see tangible potential outcomes, they are more likely to develop a positive attitude toward joining or organizing a machinery-sharing arrangement. Conversely, concerns about scheduling 2| Literature Review 29 conflicts, coordination hassles, or uneven benefit distribution can yield unfavourable attitudes (Miralles, Dentoni, and Pascucci 2017). Moreover, the evaluation of behavioural outcomes specifically addresses how farmers weigh the pros and cons of sharing. For instance, studies (Alpaslan Başarık 2015; G. Artz and Naeve 2016) indicate that if the cooperative framework provides genuine economic gains (e.g., better purchase power or mechanization) while also avoiding friction with farm routines, farmers’ attitude is more supportive. On the other hand, if they anticipate complicated conflict-resolution or minimal net gains, they will evaluate the behaviour as less worthwhile. So, attitude is usually shaped by perceived benefits, adaptability, and organization challenges. 2. Subjective Norm (Normative Beliefs and Motivation to Comply) Subjective norm in the TPB covers the perceived social pressures to either adopt or reject the new behaviour. Normative beliefs involve the farmer’s perception of how certain important reference groups view the idea of machinery-sharing or cooperatives. Empirical findings (Harris and Fulton 2000; Triandini et al. 2023) show that visible endorsements from influential community members or recognized champions strongly boost farmers’ perceived legitimacy of cooperatives. Conversely, if local gossip discourages collaborative approaches or if older farmers prefer individual ownership, normative beliefs can tilt negatively. Motivation to comply captures the degree to which farmers are willing to abide by these social expectations (Zhang et al. 2024). When farmers trust a respected local aggregator or rely on advice from a knowledgeable agricultural expert, they are more likely to follow recommendations and participate. Meanwhile, more independentminded farmers or those who distrust the endorsers might disregard social cues. In short, normative beliefs set the perceived social pressure, and farmers’ motivation to comply determines whether that pressure effectively drives them to adopt or not. 3. Perceived Behavioural Control (Control Beliefs and Perceived Power) Perceived behavioural control pertains to farmers’ sense of whether they have the means to implement the behaviour. Control beliefs refer to perceived facilitators or barriers, from logistic details like scheduling, travel distance financial constraints to intangible factors like personal negotiation skills (De Toro and Hansson 2004; Fadeyi, Ariyawardana, and Aziz 2022). If farmers believe the arrangement’s complexities, such as scheduling the equipment use, can be managed or that they have local extension support for dispute resolution, control beliefs increase. Perceived power is the farmers’ sense of their own ability or authority to manage those constraints (Gabriel and Gandorfer 2023). For instance, if a single large-scale farmer in the group dictates usage time while smaller-scale farmers feel overshadowed, those smaller farmers perceive they have little power, dampening their willingness to participate. On the contrary, when group structures or formal agreements ensure 30 2| Literature Review equitable resource access, farmers gain confidence in their power to shape outcomes. This robust sense of control is critical for forging strong intention and even final behaviour toward cooperative membership or machinery-sharing. 4. Behavioural Intention and Behaviour Taken together, attitude (and outcome evaluations), subjective norm (via normative beliefs and motivation to comply), and perceived behavioural control (based on control beliefs and perceived power) shape farmers’ overall intention to adopt a shared machinery solution or a cooperative. Once that intention is sufficiently strong the actual behaviour of engaging on a shared model often follows. However, not always is like this. Certain studies note that unexpected changes in social norms or logistical breakdowns can alter farmers’ intentions midstream (G. Artz and Naeve 2016). Although TPB helps explain attitudinal and social factors, research is still limited on how these concepts apply to small farming communities on engaging in shared models. It struggles to explain why, even with positive intentions, final engagement does not always happen. Nevertheless, the consistent finding is that each TPB component influences a farmer’s readiness to adopt to participate in shared or cooperative-based business models. However, it remains largely unexplored why, despite having the intention, farmers do not always follow through with engagement. To address this gap, this thesis will use TPB while incorporating additional questions to better identify the real drivers and barriers that influence small farmers' engagement in a shared model. 31 3 Methodology Once the context and literature have been presented, which is essential for critically evaluating the findings after the thesis investigation, the methodology that it follows is now introduced. It explains the research approach used, the frameworks it is based on, the selection sample principles, and ethical considerations. Then, it continues explaining the data collection process, how it is presented for further analysis, and how these processes are carried out. This very structured approach (following Eisenhardt method and insights of Miles & Huberman) ensures a rigorous and scientific analysis. In the different sections, it will be highlighted how the structure and iterations are conducted to ensure the rigorousness of the findings. 3.1. Research Approach This research follows a qualitative case study approach (Eisenhardt, 1989) to explore how small farmers adopt new technologies and how shared business models influence this process. Given the complexity of the subject, where technology adoption intersects with sustainable business models, this approach allows for an in-depth investigation of real-world examples, complementing and expanding existing theoretical frameworks. Case studies are particularly valuable in emerging or underexplored research areas as they enable an inductive, theory-building process (Eisenhardt, 1989). Furthermore, Eisenhardt's framework highlights the necessity of structuring data collection and analysis systematically to facilitate pattern recognition and theoretical development. This approach has been selected for this thesis, meaning a qualitative study based on case studies through interviews will be conducted. This method was chosen over others because it provides more detailed responses, helps uncover root problems, and allows for the better identification of key drivers and barriers. In contrast, a quantitative approach would require a larger sample of farmers and would not yield insights as personal and in-depth as those obtained through this qualitative method. This process follows a logical progression, explained in the next sections, which is structured around the UTAUT and TPB frameworks. 32 3| Methodology 3.2. Frameworks Utilized To enhance the explanatory power of the Unified Theory of Acceptance and Use of Technology (UTAUT) in the context of small farmers' technology adoption, this study incorporates several modifications. While UTAUT provides a strong foundation for analysing behavioural intention and use behaviour, it does not fully capture external environmental influences, financial considerations, habitual usage, or the long-term sustainability of adoption in agriculture. Therefore, this research takes advantage of the possible adaptations of the framework presented in 2.2.1.2. Improvements to the UTAUT Model, while further details on its application and modifications will be provided in chapter 4.Frameworks. These modifications ensure that the framework better reflects the economic, social, and psychological dimensions influencing small farmers’ adoption decisions. In the case of TPB, it will be used without modification as it is already a framework that captures the key psychological factors influencing farmers' decision-making, making it well-suited for analysing their behaviour toward shared business model acceptance, also developed in chapter 4.Frameworks. However, as mentioned, additional intrinsic questions will be posed to understand why the theory fail not predicting properly the connection between intention and behaviour. In both cases, the literature review has already highlighted the advantages of these frameworks over older ones. UTAUT is used for technology adoption because it focuses on the factors influencing adoption decisions and is more powerful, while TPB is applied to sustainable business models, as it better accounts for collective settings and provides a more generic approach to evaluating the intangible concept of a shared model and intention. However, this TPB broader approach, while making it easier for farmers to express their views, can lead to the loss of important detailed information. To address this, specific questions about shared business models are included to fill the gap already discussed in the literature review. 3.3. Sample Description The study sample consists of small farmers operating in diverse agricultural sectors, including crop cultivation and livestock farming. Farmers were selected based on specific criteria to ensure a representative range of perspectives and experiences: • Farm Size: Participants are small/medium scale farmers managing holdings typically below 30 hectares. • Technology Adoption Status: The sample includes farmers who have adopted, considered, or rejected new agricultural technologies. • Geographical Distribution: Farmers are drawn from 2 different locations to capture variations in economic, social and environmental conditions. 3| Methodology 33 • Business Model Involvement: Some farmers currently participate in cooperative or shared business models, while others operate independently. The selection of only small and medium-scale farmers is due to their being the focus of this study, as they face unique challenges compared to larger agribusinesses. The diversity in technology adoption provides different perspectives, which are useful for understanding how adoption patterns may vary among farmers. Same for geographic differences, is also crucial for assessing the influence of external factors, which is why two locations with distinct characteristics were chosen. Lastly, comparing the perspectives of farmers involved in sustainable business models with those who have not participated in them can provide valuable insights on the different experiences. Several additional factors could have been considered in the selection process but were not included due to feasibility constraints like quantifying the income level, financial status or generational differences. This last one because no farmers families have been found to interview. Also, the need to maintain a manageable scope made the project stick with these 4 criteria and a total quantity of 5 farmers selected. 3.4. Ethical Considerations All participants provide informed consent before interviews, ensuring voluntary participation and confidentiality. 3.5. Data Collection Eisenhardt (1989) advocates for the use of multiple data collection methods, including interviews, observations, and archival sources, to ensure triangulation. While this study primarily relies on semi-structured interviews, this approach is further reinforced by an extensive literature review and the open-ended format interviews to provide deeper insights into farmers’ decision-making processes (Miles & Huberman, 1994). This ensures a comprehensive empirical grounding for theory-building. Data collection with this format is conducted to achieve flexibility in exploring farmers' experiences, concerns, motivations, barriers, and drivers. Gathering information while giving farmers the freedom to talk about whatever they feel is necessary and ensures that we do not limit ourselves to our own assumptions or rely solely on previous studies. This approach is followed to obtain more authentic and unbiased information, which is important for this specific research that aims to identify the final drivers and barriers in the adoption technology and engage in SBM process. The interviews follow a structured approach consisting of three main sections: 1. General Information: Understanding the farmer's background, operational scale, and challenges. This questions directly relates to the UTAUT moderators. 41 4 Frameworks Already introduced the methodology, we are barely prepared to start applying it. Just before it, there’s the need of this chapter justifying the use of this specific framework and explaining the adaptations and specific use that they will have. 4.1. Reasons for using UTAUT and TPB In studying technology adoption among small farmers, existing frameworks such as UTAUT and TPB provide valuable but incomplete perspectives. UTAUT is wellsuited to analyse individual decisions to adopt new technologies, focusing on factors like performance expectancy, effort expectancy, social influence or facilitating conditions. However, it does not account for collective decision-making, the role of trust and shared behavioural norms or any factors related to group participation which are essential in shared business models. By contrast, TPB provides a stronger foundation for understanding these dynamics, as it explicitly incorporates subjective norms, attitudes, and perceived behavioural control—factors that influence adoption in group settings. Additionally, it is framed in a more generic way, making it easier for farmers to respond, especially when discussing less familiar and more intangible concepts. However, it does not evaluate individual behaviour as effectively as UTAUT. Given these complementary strengths and limitations, this research applies both models: UTAUT is used to assess technology acceptance, while TPB is applied to explore farmers' willingness to adopt a shared business model. By integrating these perspectives, the study leverages the strengths of each framework while mitigating their limitations. This hybrid use of the frameworks enables a more comprehensive understanding of adoption processes. As the study adopts an open-ended approach, UTAUT and TPB serve as analytical tools to guide data collection and analysis rather than as rigid models with predefined relationships. This approach allows findings to emerge naturally, ensuring that the study identifies the key drivers and barriers without pre-imposing assumptions about how the factors interact. The investigation then culminates in a joint analysis of the insights gained from applying these two frameworks, assessing whether findings align with existing literature or introduce new perspectives. By comparing interview responses to 42 4| Frameworks theoretical expectations, the study seeks to validate, refine, or expand the understanding of technology adoption through shared business models among small farmers. 4.2. Customizing the UTAUT Model for Small Farmers' Technology Adoption While the study aims to avoid influencing farmers' responses, adapting the UTAUT model remains necessary. This ensures the inclusion of key adoption factors relevant to small farmers' agricultural context and the specific focus of this thesis, which the original model does not fully address (Ronaghi and Forouharfar 2020; Michels, Bonke, and Musshoff 2020). To address these limitations, this study integrates additional mechanisms that enhance UTAUT’s ability to analyse technology adoption in these settings. These modifications include: 1. The addition of an exogenous mechanism (Environmental Uncertainty) to account for external conditions affecting adoption. 2. The expansion of internal behavioural predictors (Habit, Price Value, and Hedonic Motivation) to capture long-term adoption patterns. 3. The introduction of a moderating factor (Technology Familiarity) to reflect prior exposure to similar technologies. 4. The incorporation of a long-term adoption outcome (Sustained Adoption) to assess retention beyond initial adoption. All these modification methods are supported by the scientific community and have already been presented in 2.2.1.2. Improvements to the UTAUT Model. These modifications are based on existing literature and theoretical considerations, ensuring that key adoption determinants are incorporated into the study without assuming predefined interactions. Their role and significance will be determined through empirical analysis. Complementing the 2.3. Description of Which Parts of the Theories Are Already Researched in the Agricultural Sector in following sections some more details are provided on how this factor might influence. 4.2.1. Incorporating an External Predictor: Environmental Uncertainty (EU) External environmental factors are particularly relevant in the agricultural sector. Farmers operate in highly uncertain conditions, where climatic variability, market fluctuations, and policy changes can significantly impact their decision-making process. To address this, the model introduces Environmental Uncertainty (EU) as a new exogenous variable. 4| Frameworks 43 Environmental Uncertainty is defined as the extent to which external changes create instability in decision-making (Cimino et al. 2024). Any of the environmental uncertainties mentioned can shape their willingness to adopt technology by influencing how they assess the risks, benefits, and usability of technological solutions (Shi et al. 2022). EU might not directly influence Behavioural Intention (BI) but rather shape the key determinants that drive BI. Existing research suggests that EU can influence key adoption determinants such as Performance Expectancy (PE), Effort Expectancy (EE), Social Influence (SI), and Facilitating Conditions (FC). In unstable conditions, farmers may view technology as a means to mitigate risks, which can increase the importance of Performance Expectancy (PE) if the technology is perceived as a reliable solution to external challenges. However, heightened uncertainty may negatively impact Effort Expectancy (EE) if farmers perceive adoption as an additional burden in an already volatile environment. Similarly, Social Influence (SI) may become more influential, as farmers facing uncertainty are more likely to rely on peer recommendations and community norms when making adoption decisions. Additionally, Facilitating Conditions (FC) may gain importance, as farmers experiencing uncertainty might depend more on external support structures, such as financial aid or cooperative networks, to adopt and sustain the technology. Another construct that will be presented in the next section 4.2.2.Expanding Internal Predictors: Habit, Price Value, and Hedonic Motivation, is Price Value (PV), which may play a crucial role in farmers' decisions, as financial instability can heighten sensitivity to upfront costs and long-term economic benefits, making affordability a key determinant. This variable is classified as an exogenous mechanism because it originates outside the farmer’s decision-making process. Unlike internal cognitive factors, Environmental Uncertainty is not shaped by individual experiences or psychological predispositions but rather by objective external conditions that influence how technology is perceived as a tool for risk management. 4.2.2. Expanding Internal Predictors: Habit, Price Value, and Hedonic Motivation While Environmental Uncertainty represents an external force shaping adoption decisions, the model also is expanded with internal mechanisms by incorporating three additional constructs from UTAUT 2 (an extension of the framework approved by its creator, Venkatesh): Habit (HT), Price Value (PV), and Hedonic Motivation (HM). These variables influence the internal decision-making process and directly shape Behavioural Intention (BI) and Use Behaviour (UB). In the literature review section, the studies that highlight the relevance of including these constructs in the research are cited, and they serve as the justification for their 44 4| Frameworks inclusion in the study. However, the study does not assume predefined interactions between these variables and adoption outcomes; their role will be explored through empirical findings. • Habit (HT) captures the extent to which farmers develop automatic behaviours in using a technology over time (Venkatesh, Thong, and Xu 2012). Prior studies suggest that long-term adoption is not solely based on initial intention but also on the development of habitual use (Handoko Putra et al. 2023). Since habit forms through repeated exposure and usage, it is classified as an endogenous mechanism—it does not exist independently of the farmer’s own behaviour but instead emerges through experience. Thus, while past research suggests a link between habit formation and long-term use, this study will examine whether and how this process occurs among small farmers. • Price Value (PV) represents the farmer’s perception of the benefits of a technology relative to its cost (Venkatesh, Thong, and Xu 2012). Financial constraints are a major barrier for smallholder farmers, making perceived value a crucial determinant of adoption (Faridi, Kavoosi-Kalashami, and Bilali 2020). Price Value is an internal cognitive evaluation, meaning it is shaped by an individual's subjective assessment of costs and benefits. When the perceived benefits outweigh the costs, Price Value might have a positive impact on Behavioural Intention. • Similarly, Hedonic Motivation (HM) refers to the enjoyment or intrinsic satisfaction derived from using the technology (Venkatesh, Thong, and Xu 2012). Although adoption in agricultural contexts has traditionally been studied through functional or economic lenses, recent findings indicate that positive user experience can enhance adoption, particularly for mobile-based agricultural technologies (Antwi-Boampong et al. 2024). Since Hedonic Motivation is an internal psychological factor that arises from individual perceptions rather than external conditions, it is also classified as an endogenous mechanism. What literature says is that higher Hedonic Motivation will positively impact Behavioural Intention (BI), particularly among younger or more tech-savvy farmers. However, this study will consider all other possible impacts, not just this aspect. 4.2.3. Introducing a Moderating Variable: Technology Familiarity The original UTAUT model includes gender, age, experience, and voluntariness as moderating factors. Adding Technology Familiarity will capture prior exposure to similar technologies. Studies like (Handoko Putra et al. 2023) found that farmers who have previously interacted with related technologies may perceive adoption as easier and be less resistant to change so it is a good argument to consider this moderator in the study and analyse its impact. 4| Frameworks 45 Unlike exogenous predictors, which shape the decision-making environment, and endogenous predictors, which influence behavioural outcomes, Technology Familiarity functions as a moderator because it does not directly determine adoption but modifies the strength of relationships between key variables. 4.2.4. Expanding the Model with new Outcomes: Sustained Adoption (SA) Most technology adoption models, including UTAUT, focus primarily on initial adoption (Use Behaviour, UB). However, adoption does not always guarantee longterm use, and this might be a problem when treating collaborative models. Many small farmers adopt new technologies only to abandon them later due to financial difficulties, usability issues, or shifting priorities (Ronaghi and Forouharfar 2020). To address this limitation, this study introduces Sustained Adoption (SA) as an additional outcome variable. Sustained Adoption refers to the continued use of technology over time, beyond the initial adoption phase. Studying this factor is crucial, as it directly influences the long-term viability and effectiveness of a shared business model. 4.3. UTAUT and TPB as Analytical Tools Building upon the adaptations outlined in the previous section, this study applies the final structures of UTAUT and TPB as analytical tools. This means that these frameworks serve as guiding structures for data collection and analysis rather than fixed models with predetermined relationships or assumed significance of constructs. The UTAUT framework, in particular, provides a flexible base structure where the selected constructs are drawn from existing literature and related to the thesis focus, but their interactions and relevance remain subject to reassessment based on empirical findings. The connections between UTAUT constructs are not predefined, allowing the data to reveal which relationships are most meaningful or whether additional constructs should be considered based on real-world insights from small farmers. By contrast, TPB is used in its original form without modification as alternative versions have not been as widely validated. Its core components—attitudes, subjective norms, and perceived behavioural control—are considered broad enough to capture farmers’ decision-making regarding shared business models. However, while its structure remains unchanged, this study does not impose assumptions on the weight or influence of each construct before data collection. To refine the findings, as already mentioned, there are additional questions that may help interpret inconsistencies, already commented in the literature, when applying this model to the complex intrinsic aspects of SBMs. 46 4| Frameworks The following Table 4.1 expands on the definitions provided in Table 2.1 for the UTAUT concepts used in this study. Figure 4.1 presents the template of the UTAUT model to be applied, aiming to determine which factors influence adoption and sustained used, whether any additional factors should be included and how they relate to each other. Lastly, Figure 4.2 depicts the TPB in its original form; although it remains unchanged, the importance of its links still needs to be confirmed. Both frameworks serve as the core structure for the interviews and will be utilized in data collection, presentation, and analysis. Concept Definition Bibliography Environmental Uncertainty “Refers to the speed and intensity of technological change and market changes in the industry” (Lissillour et al. 2024) Habit "The extent to which people tend to perform behaviours automatically" (Venkatesh, Thong, and Xu 2012) Price Value “Consumers’ cognitive trade-off between the perceived benefits of the applications and the monetary cost for using them” (Venkatesh, Thong, and Xu 2012) Hedonic Motivation "The fun or pleasure derived from using a technology, which has been shown to play an important role in determining technology acceptance and use" (Venkatesh, Thong, and Xu 2012) Technology Familiarity “The level of prior exposure or familiarity an individual has with similar technologies” (Handoko Putra et al. 2023) Sustained Adoption Refers to the continued and long-term use of a technology beyond initial adoption Own elaboration Table 4.1: Additional UTAUT concepts and their definitions. Source: Own elaboration 4| Frameworks 47 Figure 4.1: UTAUT Template for Analysis. Source: Own elaboration Figure 4.2: TPB template for Analysis. Source: (Knauder and Koschmieder 2018) 49 5 Data Presentation As outlined in the 3. Methodology, the collected interview data follows a structured format aligned with the UTAUT and TPB frameworks to ensure consistency and facilitate analysis. While most responses fit within these models, additional sections capture insights beyond their scope. Each farmer's data is presented individually, covering: 1. Introduction: General profile (demographics, experience, voluntariness of use, technology familiarity, and challenges). 2. Technologies Used and Case Study Selection: Overview of adopted or rejected technologies for UTAUT analysis, and, if applicable, the technology considered in the TPB-based hypothetical scenario. 3. UTAUT-Based Analysis: Categorization of responses under key constructs influencing technology adoption and sustained use. 4. TPB-Based Analysis: Evaluation of attitudes, subjective norms, and perceived behavioural control regarding shared business models. 5. Intrinsic Shared Model Preferences: Additional insights into preferences and concerns beyond TPB. 6. Additional Insights: Emerging themes or unexpected findings. This structured presentation ensures a clear transition into the within-case and crosscase analysis in 6. Results and Discussion. 56 5| Data Presentation 5.3. Berta 1. Introduction Berta (27 years old) has been involved with livestock from an early age; though her formal engagement started roughly ten years ago; she grew up around farming activities near The Montseny Massif. She is close to completing her veterinary studies and is currently working in a mid-sized dairy farm with about 260 milking cows, alongside eight other workers. She also has a partner who raises beef cattle in the same facility. Despite common perceptions of economic uncertainty in the livestock sector, Berta believes that, at present, one can make a decent living. However, she notes that this can shift abruptly depending on fluctuating market prices and policies: “It can change tomorrow, and we can be ruined because you never know how the market will go”. She is also quite used to technological solutions, describing herself as more openminded than older generations might be. As she stated, “I’m not too qualified, but I try to embrace the tech. The only problem is maybe we don’t use its full potential”. 2. Technologies Used and Case Study Selection Her farm uses several technological tools, but the primary focus for this case study is the electronic collar system for dairy cows, which monitors activity levels, rumination, and potential health/heat signs. This technology was adopted to improve breeding efficiency and overall herd management. Aside from that, the farm has a standard milking parlour and certain agricultural machinery equipped with innovations (e.g., computerized feeding). • Adopted Technology for UTAUT: The collar-based sensor system • Potentially Considered but Not Adopted: Full-automation solutions (e.g., advanced robotic milking systems or further analytics) were not pursued due to cost and the complexity of training employees. 3. UTAUT-Based Analysis 1. Performance Expectancy • Expectation: Automate and improve heat detection, reduce manual observation time, and enhance breeding outcomes. • Outcome: The system has “exponentially increased fertility” because they can inseminate the cows in the optimal timeframe and detect health concerns early. • She reports that “it saves us from constantly watching all the cows and guessing if they’re in heat” 2. Effort Expectancy 5| Data Presentation 57 • Expectation: Straightforward app or computer interface, minimal learning curve. • Outcome: Berta considers the fundamentals easy but suspects there are advanced features they are not fully exploiting. “Surely there are functions we still don’t know, but we learned enough for day-to-day work”. 3. Social Influence • The decision was partly influenced by neighbours who praised the collars’ benefits. “Those who already had it told us that they were so pleased, so we thought, ‘Let’s do it too!’”. 4. Facilitating Conditions • Berta felt prepared regarding budget, labour, and basic tech know-how. There were no major connectivity issues in their barn, and the vendor provided training sessions. • She remarks that “one unqualified person can still learn it, but it helps to have some background”. 5. Hedonic Motivation • She admits it was exciting to bring modern technology to a traditionally rudimentary sector. “When something new and techy comes to the farm, it’s fun”. 6. Price Value • She found it justifiable because better reproductive performance can quickly recoup costs in a mid-sized dairy. “I’m sure if we counted the extra liters thanks to better fertility, it’s worth it”. 7. Habit • The collar system integrated well into daily routines: “I thought it would be a disruption, but it just simplified insemination planning”. 8. Environmental Uncertainty • She recognizes dairy farming is subject to market swings, but the collar system remains valuable regardless of short-term price changes. “As long as it helps keep the herd healthy and pregnant on schedule, I’ll keep using it”. 4. TPB-Based Analysis In a hypothetical scenario of sharing machinery (like a tractor or specialized harvest equipment), Berta gave her perspective: 1. Attitude Toward Behaviour 58 5| Data Presentation • She finds it practical and “ideal” in theory, especially for less frequently used or expensive machines. “Many machines are only used a few times a year, so it makes sense to share”. 2. Subjective Norm • Family and other farmers might be somewhat sceptical due to tradition or fear of conflicts, but she personally sees no major barrier. “If the sector were more cooperative, we’d all benefit”. 3. Perceived Behavioural Control • She feels fully capable of implementing a shared system. Her only concern is whether enough neighbours would commit and maintain the equipment properly. “I’d do it if everyone played fair. The problem is always trust”. 4. Behavioural Intention • Berta would be likely to experiment with a shared model if others showed genuine interest. She remains flexible with scheduling and sees synergy: “We have 24 hours in a day; we can rotate usage. The question is if people want to share”. 5. Intrinsic Shared Model Preferences Outside the formal TPB constructs, Berta provided insights on: • Financial Feasibility vs. Collective Mindset: She is open to splitting costs on expensive, seasonally used equipment. “The only problem is the typical ‘I want the biggest tractor’ mindset”. • Maintenance Concerns: She expects a straightforward system where each user performs routine checks, and any major breakdown is paid collectively. • Preference for Familiar Partners: While she would share with strangers if well organized, she prefers to do so with farmers she trusts to avoid conflicts. 6. Additional Insights • She sees bureaucracy as a bigger threat to farm viability than technology costs, pointing out that “Some days I feel the administration is the real daily battle”. • Positive Outlook on Technology: Berta does not shy away from investments if a clear return or workload reduction is evident. • Future Goals: She intends to remain active both as a veterinarian and a livestock farmer, using technology “to balance everything more efficiently”. 5| Data Presentation 59 5.4. Sancho 1. Introduction Sancho (55 years old) spent two decades supervising a furniture factory before transitioning into farming around 2005. He currently manages roughly 30 hectares of citrus (primarily oranges and mandarins), plus smaller plots of olives and carob trees, near Tortosa. He operates within a cooperative structure—partly to ensure stable sales channels, partly to gain access to technical guidance and better input prices. Sancho highlights two consistent challenges in his citrus operation: the first is pest management: An influx of new pests from outside the EU, coupled with stricter local regulations on pesticides. The second the profitability pressure: Greater foreign competition, supermarket demands, and fluctuating prices. Despite these hurdles, he is open to gradual technological improvements that promise tangible benefits, such as improved water management. He characterizes himself as “not at the forefront of innovation, but I keep an eye on useful solutions”. 2. Technologies Used and Case Study Selection Sancho’s farm uses modern variator-equipped irrigation pumps, which adjust motor speed based on actual pressure requirements, thereby saving energy. He also utilizes more conventional agricultural tools (tractors, atomizers), sometimes making incremental upgrades. While he is aware of advanced innovations (such as drones or sophisticated sensors), the costs and uncertain return have so far discouraged him from adopting them. • Chosen Adopted Technology (UTAUT Case): Variable-speed irrigation system for his citrus fields. • Potential Technology Considered but Not Adopted: High-end orchard drones or satellite-based precision tools—deemed too expensive relative to the uncertain yield improvements for a medium-scale orchard. 3. UTAUT-Based Analysis 1. Performance Expectancy • Expectation: Reduce electricity costs and optimize water flow. • Outcome: The adjustable pump system has lowered energy consumption by matching motor speed with real-time pressure. He is pleased with these gains: “You no longer run the motor at full revs when you don’t need that much pressure”. 2. Effort Expectancy • Expectation: Moderate complexity to program the variator, but worth learning for efficiency. 60 5| Data Presentation • Outcome: He initially required technical assistance, but once set up, the usage is “almost plug-and-play.” He found the learning curve “acceptable”. 3. Social Influence • Sancho mentions a local farmers’ WhatsApp group in which peers discuss new products and experiences. These conversations influenced him to invest in the variator pump. “The group was key—someone in [village] recommended it, saying the savings were real”. 4. Facilitating Conditions • The local power infrastructure is generally reliable, making it easier for him to see real benefits. The cooperative also provided partial guidance and help in sourcing the variator. • He had to handle the cost himself, but short-term financing was available. 5. Hedonic Motivation • He does not consider the new system “fun” but appreciates the sense of improvement and “peace of mind” from more stable irrigation. 6. Price Value • The investment, though not negligible, was justified by continuous savings on energy bills. “I looked at the numbers, and in a few years, I recoup the cost from the power savings alone”. 7. Habit • Integrating the pump controls into his daily routine was easy. Once the system parameters are set, it requires minimal tweaks. 8. Environmental Uncertainty • The major uncertainties for him are external competition, pests’ management and unpredictable market prices. He sees no risk to continuing use of the variator. 4. TPB-Based Analysis Asked hypothetically about joining a shared business model (e.g., co-owning large orchard machinery), Sancho offered the following insights: 1. Attitude Toward Behaviour • He is conceptually positive: “We should have done this years ago. Instead of each having half-used machines, we could share them”. 2. Subjective Norm 5| Data Presentation 61 • Family or neighbours might have mixed reactions. Some are openminded, while others “like to own the biggest tractor or new gear” leading to potential conflicts or pride issues. 3. Perceived Behavioural Control • Technically, he is confident they could manage schedules or finances if everything is well-structured. However, timing is a sticking point: “We all need the machinery at the same period for citrus. That can be complicated”. 4. Behavioural Intention • He sees a high potential for shared implements, especially those not used intensively all year (e.g., a woodchipper or certain harvest aids). Yet he worries that for tasks like pest spraying, everyone wants the atomizer in the same week. 5. Intrinsic Shared Model Preferences Sancho elaborated on additional personal preferences: • Flexibility in Scheduling: He suggests an informal approach, trusting that “we can talk and see who needs it this week, who needs it next” as long as everyone is cooperative. • Maintenance Responsibility: Ideally, each user checks the equipment after use and reports any issues. Large repairs could be split among all. • Preferred Group Size: He is open to working even with unknown farmers if contracts are clear. But “with too many people, it’s chaos. Maybe four or five is workable”. 6. Additional Insights • Cooperative Membership: Sancho is a strong proponent of cooperatives for marketing produce and obtaining inputs at lower cost. • Need for a More Organized Sector: He laments that “everyone has bits of land scattered around. If we consolidated or shared more, we’d save time and money”. • Market Challenges: External competition from countries using banned pesticides is a recurring frustration—he believes advanced technology alone cannot solve such structural issues. 62 5| Data Presentation 5.5. Joan 1. Introduction Joan (52 years old) comes from a long lineage of winegrowers— “the fifth generation” as he says it and has been formally operating in the sector since 2001. His enterprise can be categorized as a micro-winery, exploiting nearly 59 hectares of vineyards (and smaller plots with olive trees) in a protected designation of origin (DO) region near Tortosa. Though he inherited viticulture traditions from his family, he has taken a more modern, business-oriented approach, integrating vertical processes—growing grapes, elaborating wine, and commercializing it. He emphasizes three general challenges in his domain: (1) finding and retaining qualified personnel; (2) geographical isolation limiting logistics; and (3) adapting to changing market demands and stricter legislations. Despite these obstacles, he describes himself as relatively comfortable with technology, having introduced multiple innovations in the winemaking process: “We handle everything from the vineyard to the bottle, so we absolutely need the right tools, from traceability to temperature control”. 2. Technologies Used and Case Study Selection Joan’s winery invests in various technological systems to oversee fermentation, store historical data, manage logistics, and ensure regulatory compliance. These include: • Refrigerated stainless-steel tanks with digital temperature control • An advanced traceability software that monitors each batch from harvest to bottling, capturing all relevant data such as fermentation curves, interventions, and cost tracking. For the UTAUT case, we focus on this traceability system, which he adopted early on for better compliance and production management. Meanwhile, the technology he considered but did not adopt was a high-end data analytics module for automated cost forecasting and advanced real-time vineyard sensors, discarded for being too expensive relative to perceived benefits in his mid-scale operation. 3. UTAUT-Based Analysis 1. Performance Expectancy • Expectation: The traceability software would simplify fulfilling legal DO requirements, unify cost accounting, and improve product quality consistency. • Outcome: He regards it as indispensable: “It’s not just for legal compliance—knowing exactly what happens in each tank helps me make better wine.” 2. Effort Expectancy 5| Data Presentation 63 • Expectation: Some complexity was expected since it involves digitizing records that were once in Excel or on paper. • Outcome: He found the learning curve steeper initially than anticipated— “the first time you upload all your data is painful”—but once established, day-to-day tasks are “quicker and more efficient”. 3. Social Influence • As owner-manager, he felt pressure from both external regulations (DO and food safety) and a desire to appear professional to distributors. “Our customers want traceable proof we meet standards, so it’s not an option but a necessity”. 4. Facilitating Conditions • He assembled a team that includes a cellar master and an external IT consultant for proper implementation. Internet connectivity is reliable enough for software updates. He invests significantly in these supportive resources, calling them “essential for a modern winery”. 5. Hedonic Motivation • Not specifically relevant. He doesn’t find it “fun” but he acknowledges the sense of pride in presenting a robust quality-control system. 6. Price Value • The software is a continuous expense because of monthly support contracts, yet he views it as necessary: “It’s not cheap, but it saves us from chaos and potential legal fines—plus it helps with cost control. 7. Habit • Updating the traceability records has become routine. He integrated it fully, stating “It’s part of the job now—like cleaning the tanks or checking the vines”. 8. Environmental Uncertainty • Market swings (e.g., shifting consumer preferences or export challenges) remain the bigger unknowns. The software, however, continues to prove its worth by offering real-time data on production costs, enabling nimble reactions. 4. TPB-Based Analysis Asked about adopting a shared business model, such as pooling resources with other cellars for bottling lines or storage facilities, Joan’s input: 1. Attitude Toward the Behaviour 64 5| Data Presentation • In theory, “a shared bottling facility or unified logistics could be beneficial.” However, he highlights concerns about commercial confidentiality and brand independence: “I don’t want other cellars seeing my clients’ labels or volumes”. 2. Subjective Norm • He notes a general reluctance among competing wineries to share equipment. “We’re all small brands in the same region, so we guard our sales leads quite jealously”. • Family/staff opinions echo these concerns, as they fear losing control over proprietary processes. 3. Perceived Behavioural Control • Technically, he believes a shared logistic or bottling plant “makes perfect sense to reduce overhead” but the intangible issues (confidential data, marketing competition) hamper actual feasibility. 4. Behavioural Intention • Joan expresses low intention to fully adopt a broad shared model for processing or warehousing. He might consider partial steps—e.g., renting space or service from a specialized facility—but not fully ceding bottling or storage to a communal entity. 5. Intrinsic Shared Model Preferences Joan elaborated: • Confidentiality & Brand Integrity: He is especially cautious about letting competitors see product volumes, pricing, or labels. • Flexible Scheduling: A centralized bottling line as a shared model can create scheduling bottlenecks, “and we need quick reaction if an order arrives or if we see a perfect window to bottle a vintage.” • Co-Investment vs. External Service: He prefers a professional external service to co-ownership with direct local competitors. 6. Additional Insights • Expansion as Multiple Businesses in One: He underscores that running a winery is like managing three separate units: vineyard, cellar (industrial), and commercial sales. Each stage has distinct compliance and technology needs. • Personal Mindset: He acknowledges that many small wineries still rely heavily on tradition and are slower to adapt to digital solutions: “If you don’t track costs properly, you might be losing money and never know it.” 5| Data Presentation 65 • Future: He sees more digital integration in vineyard monitoring (e.g., with sensors), but only if the cost–benefit ratio is clearly positive. 72 6| Results and Discussion 5. Technology Adoption and Shared Business Models: Exploring the Connection Gabriel’s case highlights that shared business models do not necessarily promote shared technology adoption. While he embraces cooperative herd management, technology decisions remain highly individual and subsidy-dependent. 6.1.2. Jordi 1. Technology Adoption Figure 6.3: Jordi’s individual UTAUT framework. Source: Own elaboration 1. Key Findings on Behavioural Intention (BI): Behavioural Intention (BI) is strongly linked to Performance Expectancy (PE), Social Influence (SI), Facilitating Conditions (FC), and Price Value (PV), while Effort Expectancy (EE) has a moderate influence. PE has a strong impact, as Jordi initially expected technology to increase production and efficiency, making it a crucial driver of his BI. SI is also decisive, as his family’s decision not to continue the farm significantly influences his perception, reducing motivation to adopt new technologies. FC plays a key role, as Jordi frequently mentions financing constraints, stating that without grants, some implementations would not have been possible. PV also has a strong influence, as past benefits initially supported his willingness to adopt, but now, with no successor and shifting priorities, it negatively affects BI. EE has a moderate link, as training initially motivated him, but uncertainty about the future can also deter further engagement. 2. Key Findings on Use Behaviour (UB): Jordi’s BI and Use Behaviour (UB) have a moderate connection—he fully integrated the milking parlour into daily operations, but its continued use ultimately depended on broader economic factors, not just his initial intention. While the system met expectations, it did not prevent him from eventually leaving dairy farming when the 6| Results and Discussion 73 industry became less profitable. In contrast, Facilitating Conditions (FC) and UB had a strong connection—the availability of funding, and stable milk prices ensured high initial use, but once market conditions declined, use was no longer viable despite the technology’s effectiveness. 3. Key Findings on Sustained Adoption (SA): Jordi’s BI and Sustained Adoption (SA) have a moderate connection. Although the milking parlour remained useful and efficient, long-term use depended on external factors, leading him to eventually phase out dairy operations. Similarly, Price Value (PV) and SA also showed a moderate link, while the parlour was profitable at the time of adoption, declining milk prices and increased regulation later undermined its economic sustainability. Without a continued financial return, sustained use became unfeasible, making PV a critical but unstable factor in long-term adoption. 4. Environmental Uncertainty EU directly impacts Performance Expectancy (PE), Effort Expectancy (EE), Social Influence (SI), Facilitating Conditions (FC) and Price Value (PV), creating uncertainty in Jordi’s adoption decisions: PE is weakened as unpredictable policies and market instability make technology benefits uncertain. “Even if I adopt the best machines, if prices collapse or policies change, it won’t save my farm.” EE is also affected, as unstable conditions make learning new systems feel like an unnecessary burden. SI is reduced, as peers hesitate to invest, reinforcing caution. “No one around me is making big investments now. We’re all waiting to see what happens.” FC is unstable, as access to financing and subsidies is uncertain, limiting Jordi’s ability to upgrade. “If banks won’t lend or subsidies disappear, there’s no way I can upgrade my equipment.” PV is diminished, as financial insecurity makes long-term investments riskier. “Spending 50,000 € on automation only makes sense if I know I’ll still be in business in 10 years”. 5. Added exogenous variable: Farm Succession (FS) Farm Succession (FS) is a key Exogenous Mechanism added as it directly impacts Performance Expectancy (PE), Social Influence (SI), and Price Value (PV). FS weakens PE as Jordi sees no long-term benefit in adopting technology if his farm will not continue beyond him. SI is modulated, as his sons’ decision not to continue in farming significantly influences his perception of social influence, reducing the relevance of peer adoption trends. PV is also weaker, as Jordi perceives less value in investments, knowing their use will be discontinued in a few years. 74 6| Results and Discussion 2. Shared Business Model Engagement Figure 6.4: Jordi’s individual TPB framework. Source: Own elaboration 1. Key Findings on Intention to Engage in a Shared Model: • Attitude: Jordi is sceptical of cooperatives due to a negative past experience with a rabbit-farming cooperative. “You get stuck if the cooperative fails, and you lose your freedom”. • Subjective Norm: His family strongly discourages cooperative involvement, valuing independence. “At home, they’d say: ‘Better to be free than tied to something that might sink us all’”. • Control: He believes he could participate in a small-scale informal sharing arrangement but sees large cooperative structures as overly restrictive. 2. Key Findings on Actual Engagement in a Shared Model: • Jordi does engage in informal resource-sharing (e.g., lending machinery to trusted peers). • He rejects formal cooperative models due to fear of financial entanglement and loss of flexibility. “If I see a sector is going downhill, I want to exit quickly”. 3. Shared Business Model as a Vehicle for Technology Adoption 1. Drivers Influencing Technology Adoption Within Shared Models: • Shared Financial Risk and Investment: Jordi recalls that high-cost purchases (e.g., advanced milking parlours, new tractors) used to be feasible when farming income was strong. In current conditions, he notes that pooling resources could lower individual debts and make costly technology more attainable if there is genuine trust among the members. • Potential Efficiency Gains: When technology clearly boosts productivity or hygiene (as with his older milking system), it inspires him excitement. In a 6| Results and Discussion 75 shared setting, that same enthusiasm could spread, encouraging multiple farmers to adopt improvements through a shared model. • Relief from Upkeep and Maintenance: Managing modern equipment alone can be daunting. Although Jordi is reluctant to formalize partnerships, he does acknowledge that in theory, distributing maintenance and repair tasks among several farmers could help. Fewer personal headaches may make advanced tools more attractive. • Cooperative Frameworks Attracting External Support: Jordi benefitted from past public funding (e.g., “young farmer” grants). While he personally doubts current bureaucracies, a well-structured cooperative might access bigger subsidies, which could drive group technology adoption. • Peer-Based Learning and Resource Exchange: Although Jordi prefers informal arrangements (“if I know you, I might loan out equipment”), he recognizes that knowledge-sharing emerges naturally when farmers help each other. Within a trusted group, they could share best practices and adapt technology more confidently. 2. Barriers Within Shared Models: • Distrust Rooted in Past Cooperative Failures: Jordi explicitly mentions a “bad taste” from previous cooperative efforts (e.g., with rabbits). He fears financial collapses that would entangle all members: “If it fails, they might embargo everyone.” This experience makes him cautious about formal group ownership. • Obligatory Dependence and Loss of Autonomy: In Jordi’s words, once inside a cooperative, “you can’t suddenly decide to sell elsewhere.” Being tied to group decisions clashes with his “free agent” mindset—he prefers adapting or changing direction quickly when markets shift. • Complex Internal Coordination: Sharing any piece of equipment or facility (e.g., milking parlour) often requires careful scheduling of animals and tasks in the same space. Jordi points out that if multiple herds come in, logistics become tricky unless all animals are physically close, and all parties strictly coordinate usage. 3. Barriers Outside Shared Models • Market Volatility and Low Prices: Jordi emphasizes that farming revenue once supported large capital investments but now is highly unpredictable (“like the stock market”). Technology adoption becomes risky if incomes cannot guarantee cost recovery. 76 6| Results and Discussion • Bureaucratic Complexity: He finds official requirements and sanctions increasingly harsh. Frequent policy shifts make it tough to invest in modern tools; a single regulatory slip can result in heavy fines. • Age and Lack of Generational Succession: At nearly 60, Jordi sees no family members to carry on the farm. For him, major investments in advanced equipment feel unjustified without an assured future handover. • Preference for Simple, Informal Exchanges: Jordi’s comfort zone is friendto-friend arrangements: “If I know you, I might trade or loan machinery.” Scaling this into formal group ownership or multi-party contracts is less appealing in his context. • Shifting Sector Economics: Jordi underscores how drastically farming fortunes have changed. He once easily earned enough for new machinery; now, external competition and lower profit margins discourage big outlays on modern tech. 4. Deviations from Literature Review: • Past Cooperative Failures as a Deterrent: Although the literature notes trust issues, Jordi highlights a strong “bad taste” from personally witnessed cooperative collapses—making him reluctant to engage in formal group ownership again. • Preference for Informal, One-on-One Exchanges: Standard frameworks often emphasize structured cooperatives; Jordi instead describes a comfort zone of “favours and small trades” aligning more with ad-hoc mutual help than a well-defined shared model. • Farm Succession as an Underlying Concern: Jordi’s case highlights the absence of generational succession as a key factor shaping his adoption decisions. Jordi’s reluctance to invest in new technology stems from uncertainty about the farm’s future. This suggests that, for farmers without a successor, the perceived benefits of adoption may diminish, making shortterm practicality a stronger driver than long-term sustainability. 5. Technology Adoption and Shared Business Models: Exploring the Connection Jordi’s case shows that strong market conditions, rather than collaboration, drove his technology adoption. While he engages in informal agreements, he views structured cooperatives as too risky. His focus on financial independence makes shared technology investments unappealing. 6| Results and Discussion 77 6.1.3. Berta 1. Technology Adoption Figure 6.5: Berta’s individual UTAUT framework. Source: Own elaboration 1. Key Findings on Behavioural Intention (BI): Behavioural Intention (BI) is strongly linked to Performance Expectancy (PE) and Price Value (PV), with Social Influence (SI) and Facilitating Conditions (FC) playing a secondary role. PE was a decisive factor, as Berta adopted the collar system expecting improved fertility rates and herd management. PV also had a strong impact, as she believed the system would pay for itself through increased milk production. SI was not a key driver but provided a final push, as recommendations from other farmers reinforced her decision. The same case is for FC as it provided the vendor guidance to eased adoption. Other factors, such as the excitement she finds in using it (Hedonic Motivation) and its seamless adaptation to her routine (Habit), were positive aspects she highlighted but were neither critical nor determinant in her adoption. 2. Key Findings on Use Behaviour (UB): Berta’s Behavioural Intention (BI) and Use Behaviour (UB) have a strong connection, as she fully integrated the collar system into daily operations, regularly using fertility tracking features to optimize insemination timing. However, Facilitating Conditions (FC) have a moderate link to UB, as while the system is effective, its use depends on stable farm operations and access to necessary resources (moderate because it’s unlikely to change). Effort Expectancy (EE) also plays a moderate role, as limited training and familiarity with all available features constrain the system’s full utilization 3. Key Findings on Sustained Adoption (SA): Berta’s Behavioural Intention (BI) and Sustained Adoption (SA) have a strong connection, as she continues using the system due to its direct impact on farm efficiency. Price Value (PV) also has a strong link to SA, as she perceives the 78 6| Results and Discussion technology as an investment that generates financial returns. The only reason she would consider discontinuing its use is if new innovations emerge that significantly improve this PV efficiency. She foresees continued use. Despite market fluctuations in the dairy industry, she considers the collars indispensable for maintaining herd health and maximizing fertility. 4. Environmental uncertainty Environmental Uncertainty (EU) is a key Exogenous Mechanism as it directly impacts Performance Expectancy (PE) and Price Value (PV), reinforcing Berta’s confidence in her technology adoption. PE is strengthened, as she recognizes that the collar system remains effective despite external challenges, ensuring stable fertility tracking regardless of market or policy shifts. PV is also reinforced, as she perceives the technology as a reliable investment that continues to generate financial returns even in uncertain economic conditions. Unlike cases where EU discourages adoption due to poor technology stability in front of changes, in Berta’s case, it enhances the perceived reliability and value of the system due to its robustness, making sustained use more likely. 2. Shared Business Model Engagement Figure 6.6: Berta’s individual TPB framework. Source: Own elaboration 1. Key Findings on Intention to Engage in a Shared Model: • Attitude: Berta views shared models as highly practical, particularly for large, infrequently used equipment. “Many machines are only used a few times a year, so it makes sense to share.” • Subjective Norm: While she is personally open to shared models, she acknowledges cultural resistance within the farming community. “If the sector were more cooperative, we’d all benefit.” 6| Results and Discussion 79 • Control: She feels fully capable of implementing a shared system but identifies trust as the primary challenge. “I’d do it if everyone played fair. The problem is always trust.” 2. Key Findings on Actual Engagement in a Shared Model: • Berta has not participated in a formal cooperative but conceptually supports shared ownership. • She acknowledges that competition among farmers ("wanting the biggest tractor") limits collaboration, despite the clear economic advantages. 3. Shared Business Model as a Vehicle for Technology Adoption 1. Drivers Influencing Technology Adoption Within Shared Models: • Reduced Individual Investment and Risk: Berta acknowledges that modern agricultural technology—such as advanced collar systems—often involves a big financial commitment. In a shared model, multiple farmers could split these costs, making substantial tech investments less daunting. • Higher Operational Efficiency: She highlights how technology boosts daily productivity (e.g., automated heat detection for insemination). Extending this logic to shared machinery or resources, Berta sees the potential for each participant to streamline tasks without each person buying everything individually. • Better Access to Specialized Equipment: Berta’s farm occasionally uses machinery only a few months per year (e.g., planters, slurry tanks). Acquiring expensive tools jointly would let multiple farms benefit from high-quality equipment without incurring the entire purchase cost. • Positive Attitude Toward Collaboration: Conceptually, Berta strongly supports cooperative solutions— “For me, I think it’s ideal…”—to minimize waste and improve financial sustainability. She believes that if participants were genuinely committed, they could coordinate usage effectively. • Synergy with Technologically Progressive Mindset: Berta readily embraces innovation (collars, computer tracking), so in principle, she feels a collective arrangement would enable even more advanced solutions. 2. Barriers Within Shared Models: • Distrust and Social Friction: Berta notes that some neighbours fear others will abuse or break shared machinery: “It’s fine until someone damages something—then who pays?” She sees mutual suspicion as a primary hurdle preventing stable cooperative ownership. • Complex Scheduling and Coordination: Even if farmers agreed to share a resource, they must juggle peak usage times. Berta might need a tool exactly 80 6| Results and Discussion when another farmer also does, requiring negotiation and trust-based flexibility. • Resistance from Family or Partners: She emphasizes that in a family-run farm, everyone’s opinions matter: “We all have to agree.” Some relatives might be risk-averse or prefer exclusive ownership, making it tough to commit to a shared plan. • Preference for Familiarity: Berta would only consider sharing technology with trusted partners rather than unfamiliar farmers, as trust and reliability are crucial for her. She believes that without strong personal relationships, shared ownership could lead to conflicts over maintenance, scheduling, or responsibility. • Potential Legal/Contractual Entanglements: If a participant quits midproject, ownership shares become complicated. Berta acknowledges that while a clear contract would help, many farmers are wary of legal formalities, further inhibiting cooperative ventures. 3. Barriers Outside Shared Models: • Market Volatility and Uncertain Profits: Although Berta states that current dairy and beef markets are relatively favourable, she recognizes how quickly prices can plummet. High-cost initiatives—even if shared—remain risky without stable future revenues. • Ongoing Bureaucratic Burdens: Like many farmers, Berta complains about administrative demands. Adding a shared business structure could bring extra paperwork (contracts, cost splits, etc.). • Technology Dependence: Berta worries that “If everything is too digital, what if it fails?” Expensive or highly automated systems can be doubleedged if maintenance or breakdowns go beyond the group’s capacity to manage them. • Local Culture of Competition: In her region, some farmers prize having the “biggest tractor” and operate competitively rather than cooperatively. This mindset undercuts enthusiasm for joint ownership, despite any potential cost or efficiency advantages. 4. Deviations from Literature Review: • Tension Between Enthusiasm and Local Distrust: The importance of trust is well documented (De Toro and Hansson 2004), yet Berta’s enthusiasm for “ideal” sharing contrasts sharply with the local reluctance she observes— underscoring how subjective norms and attitudes toward sharing can diverge even within the same community. 6| Results and Discussion 81 • Highly Selective Collaboration: Whereas standard cooperative models assume a relatively open group (Laukkanen and Tura 2020), Berta insists on sharing only with personal contacts. This narrower approach demonstrates that farmers may adopt partial or “closed-circle” cooperation, shaped by strong social ties. • Scepticism of Over-Reliance on Advanced Tools: The literature discusses facilitating conditions and effort expectancy, yet Berta adds a “what if it all fails?” worry—an insight that advanced digital solutions can trigger fear of potential breakdowns or coverage gaps not fully addressed in typical costbenefit analyses. • Cultural Barriers Over Financial Incentives: While research highlights financial incentives as a key driver in shared models, Berta sees cultural attitudes as a major barrier. Farmers prioritize independence and competition, resisting collaboration even when resource pooling would be financially advantageous. Studies discuss cost-saving incentives as a motivator for cooperation (De Toro and Hansson 2004), but her case highlights how distrust can override economic logic, limiting shared technology adoption. 5. Technology Adoption and Shared Business Models: Exploring the Connection Berta’s case shows that technology adoption occurs independently of shared business models. Her decision to implement the collar system was driven by performance benefits rather than collaborative opportunities. While she supports shared models in theory, the reality is that she isn’t involved in any. When really considering it, she sees trust, tradition, and individualism as greater barriers, even with financial gains. Unlike some farmers, she does not reject shared ownership outright, but her willingness depends a lot on the right conditions, in this case not well evaluated by the TPB framework alone. 88 6| Results and Discussion • Attitude: Joan sees potential benefits in shared logistics and bottling facilities, but he prioritizes brand confidentiality and operational control. “I don’t want other cellars seeing my clients’ labels or volumes”. • Subjective Norm: Industry culture discourages sharing, as wineries guard their client lists and production details. “We’re all small brands in the same region, so we guard our sales leads quite jealously”. • Control: While he believes a shared model could work for logistics, bottling and storage involve too many proprietary concerns. 2. Key Findings on Actual Engagement in a Shared Model: • Joan has not engaged in a shared business model and remains unconvinced by hypothetical scenarios. • He acknowledges potential savings but believes trust barriers and confidentiality issues outweigh the cost benefits. 3. Shared Business Model as a Vehicle for Technology Adoption 1. Drivers Influencing Technology Adoption Within Shared Models: • Cost Efficiency and Resource Optimization: Joan operates an integrated winery that already requires significant capital (e.g., for traceability software, bottling lines, stainless-steel tanks). He notes that pooling resources across multiple wineries could, in theory, reduce individual overhead for expensive machinery. • Increased Operational Agility: While Joan emphasizes the importance of a rapid turnaround (e.g., bottling on short notice), a shared system with professional staffing could offer fast, on-demand services—potentially surpassing what smaller wineries can achieve alone. • Streamlined Storage and Distribution: A centralized warehouse or bottling facility could reduce redundant investments (each winery having its own partial setup). Joan points out that, on paper, “it would be economically positive.” • Willingness to Collaborate If “Done Right”: He is open to alliances that meet professional, confidentiality, and scheduling standards. His main priority is receiving timely, high-quality service that supports a quick response when a product needs bottling or shipping. 2. Barriers Within Shared Models: • Simultaneous Peak Demand: Joan notes that wineries generally need bottling or pressing equipment at roughly the same harvest window. Sharing specialized machines (e.g., de-stemmers, presses) is challenging if everyone requires them at once. 6| Results and Discussion 89 • Commercial Confidentiality Concerns: Producers often hesitate to reveal clients, target markets, or production volumes. A shared warehouse or bottling line requires showing “labels, pallets, or shipping data” which some wineries deem too sensitive. • Competitive / Individualistic Mindset: Although cooperatives exist in other agricultural fields, the wine sector often features numerous small, independent brands fiercely competing for limited consumer attention. This climate can impede the trust required for equipment co-ownership. • Complex Contractual Commitments: Setting up a joint facility (e.g., a multiwinery bottling plant) entails legal structures to handle buy-ins, usage fees, exit clauses, and expansions. Joan stresses the necessity of “strong professionalization and clarity” in these agreements. 3. Barriers Outside Shared Models: • Regulatory Complexity: Laws demand rigorous traceability and labelling. Although a communal approach might lower costs, it also requires advanced compliance systems that can vary across different wineries’ product lines. • Geographical and Logistical Constraints: Joan’s winery is in a remote region, where transport is slow and labour is scarce. Even if a shared bottling or storage facility existed, distance and timing might render it impractical for day-to-day use. • Culture of Autonomy and Market Volatility: Wine production is deeply tied to individual identity, as brand reputation and premium positioning rely on unique cellar processes and estate-specific terroir. Market volatility and shifting consumer preferences further reinforce the need for differentiation, making standardization or shared solutions less appealing. Surrendering aspects of production to a communal facility risk diluting a winery’s distinct identity, which is essential for maintaining exclusivity and competitive advantage. 4. Deviations from Literature Review: • Brand Confidentiality vs. Shared Efficiency: Joan underscores high-end wine producers’ reluctance to expose labelling or distribution details. This dimension of “competitive confidentiality” is only briefly noted in broader cooperative studies and is less common among small farmers. • Integrated Multi-Stage Production: Studies on machinery sharing often focus on farmers adopting a single technology (e.g., digital machinery) for specific tasks (Artz & Naeve, 2016). However, Joan’s vertically integrated model—covering vineyard management, winemaking, bottling, and distribution—demands multiple specialized tools across different 90 6| Results and Discussion production stages, making “one shared machine” models less applicable and more complex to implement. • Demand for On-Demand Access: While scheduling conflicts are acknowledged (De Toro and Hansson 2004), Joan’s push for near-instant availability raises the bar: shared solutions must function “within days” leaving minimal tolerance for typical rotation-based approaches or smaller co-op scheduling norms. 5. Technology Adoption and Shared Business Models: Exploring the Connection Joan integrates technology when it enhances efficiency but remains sceptical about shared models mainly due to brand security concerns. While he acknowledges the potential for logistical cooperation, he does not see shared production as viable, as maintaining control over winemaking processes is crucial for branding and market positioning. Unlike other farmers who hesitate due to unfamiliarity with shared models, Joan’s reluctance is strategic, driven by industry-specific challenges rather than a general resistance to collaboration. 6.2. Cross-Case Analysis While the within-case analysis provided an in-depth interpretation of individual decision-making processes, this cross-case analysis adopts a comparative perspective to enhance the generalizability and theoretical contribution of the study. By combining individual UTAUT frameworks, a generalized UTAUT framework will be developed, allowing for the study of moderators. However, its reliability will be limited due to the small sample size. This limitation will be addressed in 6.4.Discussion, where findings will be further examined in relation to the literature to either confirm or challenge findings. The same approach applies to TPB. Additionally, the part of the analysis where shared business models are studied as a vehicle for technology adoption will consolidate the drivers and barriers identified in the within-case analysis, grouping them to highlight the most common ones. It will also determine whether any unique or case-specific barriers emerge due to contextual factors. As a difference with the within-case scenario, the comparison with the literature will be conducted in the 6.4.Discussion in as the last iteration, providing validation and a deeper reflection on the study's findings. 1. Cross-Case Patterns in Technology Adoption To create the combined framework, qualitative data will be quantified in a simple way using fixed score assignment (Solid = 2, Dashed = 1, No Line = 0). Each connection, as repeated across the five farmers, will be summed. This means the maximum possible 6| Results and Discussion 91 score for a connection is 10, while a connection that is never mentioned receives a 0. To define the thresholds, connections scoring 6 or greater will receive a solid line (indicating that at least three farmers consider it important), while connections scoring between 3 (inclusive) and 6 will receive a dashed line (ensuring that at least two farmers mention it, with at least one assigning high importance, or that three consider it moderately important). The following Table 6.1 contains those connections that are significant when small farmers adopt a new technology: Factor A Factor B Score PV BI 10 PE BI 9 FC UB 9 BI SA 8 BI UB 8 PV SA 7 FC BI 6 SI BI 6 EU PE 6 EU PV 6 EU FC 4 EE UB 3 Table 6.1: Construct connections and rating. Source: Own elaboration Besides the link between constructs, the moderator effect has also to be considered. They are presented in a common table to identify potential patterns that may influence the UTAUT collective framework. Each moderator is divided into two groups, and these groups will be analysed to determine whether substantial differences exist, suggesting a possible moderating effect. While the findings may not be highly conclusive, they will provide insights that can be compared with the literature to assess their relevance. The justification of adding Geographical Region is below the tables. Check Table 6.2. Moderator Grouping for UTAUT. White cells represent Group One, while light blue cells represent Group Two. Note that grouping is done by 92 6| Results and Discussion variable, meaning each farmer is categorized independently within each variable, rather than being assigned to a single overall group. Farmer Technology Familiarity Age Gender Experi ence (years) Voluntarines s of Use Geographical Region Gabriel Low 42 Male 4 High Montseny Jordi Moderate 60 Male 42 High Montseny Berta Moderate 27 Female 10 High Montseny Sancho Moderate 55 Male 20 High Tortosa Joan Moderate 52 Male 50 Moderate Tortosa Table 6.2. Moderator Grouping for UTAUT. Source: Own elaboration Here are the grouping criteria for each variable: • Technology Familiarity: Low (No prior exposure) vs. Moderate (Some prior exposure but not extensive). • Age: Younger (≤50 years old) vs. Older (>50 years old). • Gender: Male vs. Female. • Experience (years): Less Experienced (≤20 years) vs. More Experienced (>20 years). • Voluntariness of Use: High (Self-driven adoption) vs. Moderate (External factors influence adoption). • Geographical Region: Montseny vs. Tortosa The study aimed to assess whether technology familiarity, age, gender, experience, voluntariness of use and geographical region influence the relationships between different Factor A to Factor B pairs. To do this, a moderation analysis was conducted using multiple linear regressions, where each pair was analysed separately, and regressions were run independently for each moderator. However, the analysis did not yield significant results due to limited observations per group and, in cases where regression was possible, no moderator showed a statistically significant p-value (p < 0.05). Given these limitations, a qualitative approach based on interview responses was chosen given is the best alternative. The results of applying this line are: • Technology Familiarity: Farmers with moderate technology familiarity (e.g., Berta, Jordi, Joan) tend to rely more on peer influence while those with low tech familiarity (Gabriel) show more reluctance and primarily use technology when 6| Results and Discussion 93 external factors like subsidies facilitate adoption. So, it is assumed that technology familiarity moderates the relationship between Social Influence (SI) and Facilitating Conditions (FC) with Behavioural Intention (BI). • Age: Older farmers (Jordi, Sancho, Joan) might exhibit more scepticism before acquiring a new technology. Younger farmers (Berta) display greater openness to innovation and express fewer concerns about effort expectancy. Even Jordi showed in the interview that when he was younger EE wasn’t even considered a difficulty: "If you are excited about something, you can learn to fly a plane." For this reason, age is considered to moderate the relationship between Effort Expectancy (EE) and Behavioural Intention (BI). As the link EE to BI hasn't been a significant one, this connection isn’t shown in the diagram. • Gender: studying Berta’s profile as unique representative of the females, no significant moderation has been identified compared to the male group. • Experience: Considering the group of high experienced farmers (Jordi and Joan) the common trait compared to those with lower experience was the evaluation to PV. Higher experience, more risk-averse become and only adopt if they see strong cost-benefit impact. Lower groups value it also a lot, but interviews showed that in some cases its adoption was produced even no clear benefits were shown. So, in our cases, PV was a significant factor in all the cases but, this detected pattern might be influential in other cases. So, experience might moderate the relationship between Price Value and Behavioural Intention. • Voluntariness of use: The voluntariness of use in our cases is influenced by external factors. Joan, for instance, is moderately obligated by policies. When adoption is not voluntary, Facilitating Conditions (FC) become more critical to ensure that Use Behaviour is possible. This explains why Joan evaluated the situation and realized he needed to hire a technical team to facilitate a smooth implementation, ensuring that all needed conditions were met. So, voluntariness of use moderates the interaction between FC and UB making it strictly important in non-voluntary adoptions. • Geographical Region (Added moderator): There are many reasons to include Geographical Region as a moderator. Tortosa is a more structured and policy driven environment due to the activities done there, very common agriculture (Joan & Sancho). In Montseny little farmers are more community-based and less structured. The effect of Facilitating Conditions on BI in Montseny it’s less important as it’s more self-driven while in Tortosa it’s the opposite. Social Influence to BI connection is moderated by how competitive the region is: in Gabri, Jordi & Berta’s more informal cooperations are found, while Joan & Sancho operate in a more competitive environment. Lastly, Joan & Sancho 94 6| Results and Discussion require clearer financial benefits before adopting (Price Value to BI), whereas Gabri, Jordi & Berta take more adoption risks. Taking into consideration the constructs rating system and the moderator’s analysis, the Generalized UTAUT framework is shown in Figure 6.11. Figure 6.11: Generalized UTAUT framework for small farmer’s Technology Adoption. Source: Own elaboration 2. Cross-Case Patterns in Shared Business Model Engagement Similarly to UTAUT, a generalized framework will be presented (see figure Figure 6.12). In this case, the counting scale is a red thumb-down is counted as -1, a midthumb as 0, and a green thumb-up as 1. This approach allows us to determine the combined TPB framework based on the responses of the interviewed farmers. Figure 6.12: Generalized TPB framework for small farmer’s engagement in a Shared Business Model. Source: Own elaboration 6| Results and Discussion 95 The evaluation of UTAUT and TPB generalized frameworks will be in 6.3. Results Summary and further reflected in 6.4. Discussion. 3. Shared Business Model as a Vehicle for Technology Adoption: Comparative Findings In this section, the drivers and barriers identified in the within-case analysis have been grouped. This categorization allows for organizing the most common drivers and barriers under broader conceptual umbrellas, ensuring that all identified factors are systematically classified. However, particular drivers and barriers are also considered important, as they highlight specific challenges that individual farmers face. The fact that they are less common does not make them any less significant and they are available in the 3.7.1.Within-Case Analysis. The generic drivers and barriers will be presented in table format for a better understanding: • Drivers are the motivations identified in farmers' interviews for adopting new technology through a shared business model. See Table 6.3: Drivers Description Subdivis ion (if applicabl e) Case Origin 1. Cost Sharing Farmers see cost sharing as a way to make technology adoption more affordable by splitting expenses among multiple users instead of bearing the full cost alone. Cost Pooling for Major Investments (SANCHO) Cost Efficiency and Resource Optimization (JOAN) Reduced Individual Investment and Risk (BERTA) 2.Financial Risk and subsidies Farmers see financial risk reduction and subsidies as key factors in making technology adoption more feasible. By distributing financial responsibility among multiple users or securing external funding, they can lower individual debt risks and ensure more stable investments. Access to subsidies further eases the financial burden. Potential for Ongoing Subsidies (Gabri) Shared Financial Risk and Investment (JORDI) Cooperative Frameworks Attracting External Support (JORDI) 96 6| Results and Discussion 3. Efficiency Gains Farmers see efficiency gains as a major motivation for adopting technology through shared models, as it helps them optimize resources and improve productivity. Scaling up operations becomes more manageable, improving efficiency allows tasks to be completed faster and with better results, and reducing lost time on non-value-adding activities. Additionally, farmers highlight greater agility, improved logistics efficiency, reduced idle time, and better quality as key benefits of this approach. Scale-up Greater Operational Scale (Gabri) Efficiency Potential Efficiency Gains (JORDI) Higher Operational Efficiency (BERTA) Lost time Relief from Upkeep and Maintenance (JORDI) Agility Increased Operational Agility (JOAN) Logistics Streamlined Storage and Distribution (JOAN) Idle time Maximizing Equipment Usage (SANCHO) Quality Better Access to Specialized Equipment (BERTA) 4. Peer Learning Farmers see peer learning as an important motivation for adopting technology through shared models, as it allows them to exchange knowledge and gain confidence in using new tools. Learning from others reduces uncertainty, speeds up the adoption process, and sometimes helps them recognize new tools or features that could be useful. Peer Support and Learning (Gabri) Collective Learning and Tech Upgrades (SANCHO) Peer-Based Learning and Resource Exchange (JORDI) 6| Results and Discussion 97 5. Attitude Farmers good attitude is a driver of adoption. A positive attitude toward collaboration encourages farmers to work together and share resources and a technologically progressive mindset makes them more open to innovation and new solutions. Positive Attitude Toward Collaboration (BERTA) Synergy With Technologically Progressive Mindset (BERTA) Willingness to Collaborate If "Done Right" (JOAN) General Openness to Collaboration (SANCHO) 6. Negotiatio n power Not that extended but being part of a cooperative makes farmers gain stronger bargaining power, allowing them to negotiate lower prices, better service agreements, and improved access to technology, a driver that Sancho recognized. Stronger Bargaining Power (SANCHO) Table 6.3: Drivers identified as key motivations for small farmers to adopt technology through a shared business model. Source: Own elaboration • Barriers Within Shared Models are the challenges identified in farmers' interviews that hinder the adoption of new technology within a shared business model, see Table 6.4: Barriers Within Shared Models Description Subdivi sion (if applica ble) Case Origin 1. Distrust Farmers see distrust as a key barrier to adopt technology through shared models. It can arise from scepticism toward other farmers behaviour or on On other Farmers Uncertain Interpersonal Dynamics (GABRI) Distrust and Social Friction (BERTA) 104 6| Results and Discussion research, their role has often been overlooked. This study provides a clear, empirical demonstration of Price Value’s importance, which represents a valuable contribution to the field. Similarly, the introduction of Sustained Adoption as a key outcome variable is a novel addition to the literature, as it has not been extensively studied. External factors are often overlooked in adoption models, but this research identifies Regulatory Pressure, Farm Succession, and Technological Limitations (e.g., unstable GPS connections) as relevant influences. Most notably, Environmental Uncertainty emerges as a critical external factor, significantly shaping several key constructs. This finding is particularly important, as it underscores the need to integrate contextual variables into adoption models. Finally, the role of moderators in this study presents some deviations from previous research. Technology Adoption Experience was introduced as a moderator, as prior studies had recognized its influence, yet it was rarely considered in UTAUT-based research. Its inclusion in the model demonstrated a tangible impact. Similarly, Geographical Region proved to be an influential moderator. Experience has traditionally been viewed as a stronger moderating factor than Age, this study confirms that Experience plays a more significant role. In contrast, Voluntariness of Use—despite being frequently noted in the literature—has been less studied in UTAUT, and this research confirms its relevance. As prior studies suggested, Voluntariness of Use may lead to different behavioural patterns, reinforcing its importance in technology adoption frameworks. The final moderator to discuss is gender. While the literature suggests that Gender could act as a moderator, the findings of this study indicate otherwise. This may be due to the socioeconomic and cultural context of the study, as it was conducted in a region with lower gender inequalities compared to other parts of the world. As a result, Gender did not emerge as a significant moderating factor in this research. Regarding the study of small farmers intention and behaviour in engaging with shared business models through TPB, as previously mentioned, this approach is less common in the literature. The general findings in the thesis suggest that Subjective Norms remain the most critical aspect that needs improvement to foster a more positive Intention to engage with these models. In contrast, Attitude and Perceived Behavioural Control show slightly more favourable trends. These findings are underexplored due to the limited application of this framework in prior research but interesting because show in what to focus if it is wanted to improve the Intentions to engage with a SBM. Furthermore, the case study analysis demonstrates that Intention does not always translate into actual engagement Behaviour. Some participants who expressed a negative opinion or low intention still engaged in shared business models, while others with a positive intention ultimately did not participate in practice. 6| Results and Discussion 105 The reasons behind these misalignments are examined through the analysis of drivers and barriers. The literature review presents a list of factors influencing small farmers technology adoption, and many of them are consistent with the findings in the thesis. However, the literature suggests that some of the identified barriers, when put into practice, do not always materialize. For instance, Complex Scheduling and Coordination, often considered a challenge, in some cases is more of a perceived issue than a real barrier (G. Artz and Naeve 2016). Nevertheless, the identified drivers and barriers in this study are noteworthy, as some are unique or rarely discussed in prior research. Examples include Age and Lack of Generational Succession (JORDI) and Technology Dependence (BERTA). The primary contribution of this thesis regarding drivers and barriers lies not in providing a general overview—as many of these factors are already recognized in previous studies—but in identifying specific, context-dependent drivers and barriers. Existing research often focuses on broad generalizations, which can dilute attention from the root causes of adoption challenges. By focusing on within-case studies, this research highlights specific problems that, although less frequently mentioned in studies, can be just as significant as the commonly cited barriers. Therefore, the contribution of this thesis is not only in confirming general findings but also in offering a deep, case-specific analysis that provides a more nuanced understanding of the real-world complexities affecting this model of innovation adoption focused on small farmers. The last important finding of this investigation, which is not well addressed in the literature, is the discover of no correlation between openness to adopt new technology and willingness to participate in shared business models, as evidenced by the diversity of cases studied. 6.4.2. Theoretical and Practical Implications The theoretical implications of this thesis lie in the expansion of UTAUT and TPB within the agricultural context, particularly for small farmers. It contributes to the literature by validating new constructs such as Price Value, Sustained Adoption, and Environmental Uncertainty, which have proven to be crucial in explaining small farmers' decision-making processes. Additionally, it identifies new key moderators that are typically overlooked, such Technology Familiarity or Geographical Region, as well as the role of external factors in shaping adoption behaviour. Furthermore, this research has highlighted the general negativity of Subjective Norms and it’s influence decreasing the Intention to engage in a SBM. A more detailed classification of drivers and barriers are included, both within and outside the sharing culture, offering a clearer understanding of the factors influencing farmers' engagement with shared business models to adopt innovations. Lastly, the 106 6| Results and Discussion dissociation between openness to technology and willingness to share resources introduces practical implications, which are discussed in the following paragraph. This research introduces several practical implications that can guide policymakers, industry stakeholders, and farmers toward more effective adoption of technology and shared business models. Policymakers can leverage these findings to design subsidies and regulatory frameworks tailored to small farmers’ specific needs. Additionally, the insights provide strategic recommendations for developing structured shared business model organizations, ensuring they align with farmers' real-world challenges. Another crucial aspect is the need to improve infrastructure and accessibility to prevent inequalities in the implementation of these models, ensuring that all farmers, regardless of location or resources, can benefit. Moreover, the research highlights the importance of education and awareness, emphasizing how sustainability and economic advantages can encourage farmers to engage in sharing practices and sustainable business models. Finally, technology providers and industry stakeholders can integrate these insights into their value creation, delivery, and capture strategies, adapting their offerings to support the feasibility and attractiveness of shared business models in the agricultural sector. 6.4.3. Limitations Despite the valuable contributions of this research, several limitations should be acknowledged. One key limitation is the limited sample size and regional scope. With only five case studies, the research provides in-depth qualitative insights, but this inherently restricts the generalizability of the findings. Furthermore, the focus on only two regions may not fully capture variations in technology adoption and shared business model engagement across different geographical, economic, or cultural contexts. Agricultural regions with distinct market structures, cooperative traditions, or policy environments might exhibit different patterns. Another constraint is the qualitative nature and subjectivity of the data. While the interview-based approach allows for rich and detailed perspectives, it remains subjective and may not always be fully representative of broader trends. Additionally, the study explores potential moderating effects—such as Technology Familiarity, Experience, and Geographical Region—but due to the small sample size, statistical validation was not feasible. As a result, these moderating effects remain qualitative observations rather than statistically confirmed influences. Moreover, it is important to acknowledge the pre-existing limitations of TPB and UTAUT, which have been extensively documented in prior studies. Beyond methodological constraints, external economic and policy changes could significantly alter the adoption landscape observed in this research. Market fluctuations, regulatory shifts, or changes in subsidy programs may influence small 6| Results and Discussion 107 farmers’ willingness to adopt new technologies or participate in shared business models, potentially redefining the relevance of some barriers and drivers identified in this study. Finally, another limitation is the study’s focus on machinery-sharing and cooperatives, which constrained the exploration of other shared business models. Other emerging models, such as land sharing, knowledge-sharing networks or community-supported agriculture (CSA), were not analysed in detail. Investigating a broader range of shared models could provide additional insights into their feasibility, challenges, and potential benefits for small farmers. 109 7 Conclusions and Recommendations This study set out to explore the drivers and barriers influencing small farmers’ decisions to adopt new technologies and engage with Shared Business Models. By applying the UTAUT framework for technology adoption and the TPB for understanding collective behaviours in front of shared models, it has been demonstrated that individual factors (such as Price Value and Performance Expectancy), external elements (like Environmental Uncertainty) and the action of Moderators intersect to shape the feasibility and sustained use of a technology, in independent and collaborative approaches to agricultural innovation. In particular, the thesis findings confirm that Price Value—the perceived cost-benefit ratio—exerts a strong influence on Behavioural Intention and Sustained Adoption. Meanwhile, Effort Expectancy, although traditionally linked only to Intention, appeared to have a direct albeit minor effect on Use Behaviour in certain cases. The role of moderators such as Technology Familiarity, Experience, Geographical Region, and Voluntariness of Use also emerged as context-dependent factors that can magnify or dampen adoption dynamics. The confirmation of the influence of Performance Expectancy, Social Influence, and Facilitating Conditions on Behavioural Intention—along with the latter’s impact on Use Behaviour—is also among the conclusions. Additionally, the external effect of Environmental Uncertainty is concluded to shape adoption-related evaluations in a more subtle and indirect manner. From the perspective of shared business models, Subjective Norms proved especially critical and negative: even when farmers see potential benefits. Simultaneously, individual Attitude and Perceived Control were often positive, suggesting that collaboration may be conceptually accepted, but hindered by a list of barriers contrasting with the advantages of the drivers list. The practical recommendations are addressed to: • Policymakers and public institutions: Develop targeted subsidies and grants to encourage the adoption of shared models, streamline bureaucratic procedures, and invest in infrastructure. • Agricultural Cooperatives and Associations: Formalize governance structures and provide clear solutions on the common conflicts, implement flexible 110 7| Conclusions and Recommendations scheduling systems tailored to sector needs, and educate stakeholders on the benefits of shared business models. • Technology Providers: Offer cost-effective solutions, including shared models that accommodate small farmers’ limited capital, provide local technical support, and offer training programs to ensure farmers can fully utilize the technology. • Farmers and Farmer Groups: Begin with informal exchanges to build trust gradually before committing to a shared business model, practice open communication and transparency to prevent disputes, and leverage collective bargaining power. • Other Interested Stakeholders: Use this research as a resource to generate positive societal impact by fostering knowledge and collaboration in the agricultural sector. Several avenues for future research remain: • Longitudinal Studies: Tracking sustained adoption over multiple seasons could clarify how economic fluctuations or policy changes influence the continued use of shared technology. • Comparative Studies Across Regions: Expanding the sample to include additional geographical areas would provide insights into regional moderating effects. • Alternative Shared Models: Future investigations could explore other shared models—such as knowledge-sharing networks, community-supported agriculture, or peer-to-peer marketplaces—to determine whether similar benefits and barriers apply. • Non-Adopters: A deeper understanding of farmers who completely reject both technology and shared models would help identify absolute barriers to innovation, informing policy interventions aimed at the most resistant segments. The final reflexion for this thesis is that the global push for sustainable and resilient agriculture will likely continue. Placing small farmers at the forefront of innovation and partnership opportunities is complicated but must be done. By recognizing the multifaceted barriers and drivers documented in this study, stakeholders can tailor policies, technologies and cooperative structures to better address small farmers’ economic realities and cultural preferences, thereby ensuring they remain competitive, sustainable, and economically viable in the long term. 111 Bibliography Ajit Niranjan. 2024. ‘“We Didn’t Realise How Hard It Is”: Small Farmers in Europe Struggle to Get By’. The Guardian, 3 November 2024. https://www.theguardian.com/environment/2024/nov/03/we-didnt-realisehow-hard-it-is-small-farmers-in-europe-struggle-to-get-by. Ajzen, Icek. 1991. ‘The Theory of Planned Behavior’. Organizational Behavior and Human Decision Processes 50 (2): 179–211. Al Fagih, Khaled. 2022. ‘INVESTIGATING THE ADOPTION OF AN INNOVATION USING AN EXTENDED UTAUT MODEL: THE CASE OF MOBILE LEARNING TECHNOLOGY’. Journal of Theoretical and Applied Information Technology 100 (July). Al-Gahtani, Said, Geoffrey Hubona, and Jijie Wang. 2007. ‘Information Technology (IT) in Saudi Arabia: Culture and the Acceptance and Use of IT’. Information & Management 44 (December):681–91. https://doi.org/10.1016/j.im.2007.09.002. Alpaslan Başarık, Saadettin Yıldırım. 2015. ‘A Case Study of Sharing Farm Machinery in Turkey’. 2015, 2015. https://www.researchgate.net/publication/334364284_A_Case_Study_of_Shari ng_Farm_Machinery_in_Turkey. Antwi-Boampong, Ahmed, Boison King David, Blay Augustine, Dominic Kofi Louis, and Ebenezer Malcalm. 2024. ‘Exploring Behavioral Drivers Behind Farmers’ Willingness to Embrace the Internet of Things’. International Journal of Religion 5 (11): 7518–35. https://doi.org/10.61707/m9ajdd79. Artz, Georgeanne M., Gregory Colson, and Roger G. Ginder. 2010. ‘A Return of the Threshing Ring? A Case Study of Machinery and Labor-Sharing in Midwestern Farms’. https://doi.org/10.22004/AG.ECON.100515. Artz, Georgeanne, and Linda Naeve. 2016. ‘The Benefits and Challenges of Machinery Sharing Among Small-Scale Fruit and Vegetable Growers’. Journal of Agriculture, Food Systems, and Community Development, 1–17. https://doi.org/10.5304/jafscd.2016.063.003. Brenner, Barbara, and Daria Drdla. 2023. ‘Business Model Innovation toward Sustainability and Circularity—A Systematic Review of Innovation Types’. Sustainability 15 (15): 11625. https://doi.org/10.3390/su151511625. 112 | Bibliography Cimino, Antonio, Ilda Maria Coniglio, Vincenzo Corvello, Francesco Longo, Juliana Keiko Sagawa, and Vittorio Solina. 2024. ‘Exploring Small Farmers Behavioral Intention to Adopt Digital Platforms for Sustainable and Successful Agricultural Ecosystems’. Technological Forecasting and Social Change 204 (July):123436. https://doi.org/10.1016/j.techfore.2024.123436. Colapinto, Cinzia, Raja Jayaraman, Fouad Ben Abdelaziz, and Davide La Torre. 2020. ‘Environmental Sustainability and Multifaceted Development: Multi-Criteria Decision Models with Applications’. Annals of Operations Research 293 (2): 405– 32. https://doi.org/10.1007/s10479-019-03403-y. Curtis, Steven Kane, and Matthias Lehner. 2019. ‘Defining the Sharing Economy for Sustainability’. Sustainability 11 (3): 567. https://doi.org/10.3390/su11030567. Curtis, Steven Kane, and Oksana Mont. 2020. ‘Sharing Economy Business Models for Sustainability’. Journal of Cleaner Production 266 (September):121519. https://doi.org/10.1016/j.jclepro.2020.121519. Davis, Fred D. 1989. ‘Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology’. MIS Quarterly, 319–40. De Toro, A., and P.-A. Hansson. 2004. ‘Machinery Co-Operatives—a Case Study in Sweden’. Biosystems Engineering 87 (1): 13–25. https://doi.org/10.1016/j.biosystemseng.2003.10.010. Dhillon, Rajveer, and Qianna Moncur. 2023. ‘Small-Scale Farming: A Review of Challenges and Potential Opportunities Offered by Technological Advancements’. Sustainability 15 (21): 15478. https://doi.org/10.3390/su152115478. Donner, Mechthild, and Hugo De Vries. 2023. ‘Business Models for Sustainable Food Systems: A Typology Based on a Literature Review’. Frontiers in Sustainable Food Systems 7 (May):1160097. https://doi.org/10.3389/fsufs.2023.1160097. Eckhardt, Andreas, Sven Laumer, and Tim Weitzel. 2009. ‘Who Influences Whom? Analyzing Workplace Referents’ Social Influence on It Adoption and NonAdoption’. JIT 24 (February):11–24. https://doi.org/10.1057/jit.2008.31. Environmental Working Group. 2024. ‘USDA Census: Smaller Farms Falling Further Behind’. 13 February 2024. https://www.ewg.org/news-insights/newsrelease/2024/02/usda-census-smaller-farms-falling-further-behind. Fadeyi, Oluwamayokun Anjorin, Anoma Ariyawardana, and Ammar Abdul Aziz. 2022. ‘Factors Influencing Technology Adoption among Smallholder Farmers: A Systematic Review in Africa’, January. https://doi.org/10.17170/KOBRA202201195569. Faridi, Amir Ali, Mohammad Kavoosi-Kalashami, and Hamid El Bilali. 2020. ‘Attitude Components Affecting Adoption of Soil and Water Conservation Measures by | Bibliography 113 Paddy Farmers in Rasht County, Northern Iran’. Land Use Policy 99 (December):104885. https://doi.org/10.1016/j.landusepol.2020.104885. Gabriel, Andreas, and Markus Gandorfer. 2023. ‘Adoption of Digital Technologies in Agriculture—an Inventory in a European Small-Scale Farming Region’. Precision Agriculture 24 (1): 68–91. https://doi.org/10.1007/s11119-022-09931-1. Gholian-Jouybari, Fatemeh, Mostafa Hajiaghaei-Keshteli, Arya Bavar, Avesta Bavar, and Behzad Mosallanezhad. 2023. ‘A Design of a Circular Closed-Loop AgriFood Supply Chain Network—A Case Study of the Soybean Industry’. Journal of Industrial Information Integration 36 (December):100530. https://doi.org/10.1016/j.jii.2023.100530. Guyader, Hugo, and Laura Piscicelli. 2019. ‘Business Model Diversification in the Sharing Economy: The Case of GoMore’. Journal of Cleaner Production 215 (April):1059–69. https://doi.org/10.1016/j.jclepro.2019.01.114. Handoko Putra, Yeffry, Lia Warlina, Dina Fatimah, Wantoro, and Selfa Septiani Aulia. 2023. ‘Adoption of the Agriculture Application by Farmers Using the UTAUT2 Method Focused on Community Behavior and User Experience’. International Journal Of Computer Sciences and Mathematics Engineering 2 (2): 52–65. https://doi.org/10.61306/ijecom.v2i2.22. Harris, Andrea, and Murray E. Fulton. 2000. Farm Machinery Co-Operatives: An Idea Worth Sharing. Saskatoon: Centre for the Study of Co-operatives, University of Saskatchewan. Hong, Insu, and Changsok Yoo. 2020. ‘Analyzing Spatial Variance of Airbnb Pricing Determinants Using Multiscale GWR Approach’. Sustainability 12 (11): 4710. https://doi.org/10.3390/su12114710. Housman, Jeff. 2003. ‘Understanding Female Collegiate Athletes’ Intentions to Consume Sports Supplements: An Application of the Theory of Planned Behavior’, May. Im, Il, Seongtae Hong, and Myung Soo Kang. 2011. ‘An International Comparison of Technology Adoption Testing the UTAUT Model’. Information & Management 48 (January):1–8. https://doi.org/10.1016/j.im.2010.09.001. Knauder, Hannelore, and Corinna Koschmieder. 2018. ‘Individualized Student Support in Primary School Teaching: A Review of Influencing Factors Using the Theory of Planned Behavior (TPB)’. Teaching and Teacher Education 77 (October):66–76. https://doi.org/10.1016/j.tate.2018.09.012. Lan, Jing, Yuge Ma, Dajian Zhu, Diana Mangalagiu, and Thomas Thornton. 2017. ‘Enabling Value Co-Creation in the Sharing Economy: The Case of Mobike’. Sustainability 9 (9): 1504. https://doi.org/10.3390/su9091504. 120 A| Appendix: Interviews template Section 2: Technology adoption - Which Equipment, Software or Any data tracking, sensors, analysis system have you acquired? - Which Equipment, Software or Any data tracking, sensors, analysis system have you considered but at the end not acquired? - 2 Study case (1st an acquired, 2nd a not acquired): 1. Voluntariness of use • Yes or no 2. Performance expectations • What did you expect from it, in terms of performance? (Quicker, productivity, less issues…) • Has it achieved it? Any critics? 3. Effort expectations • What did you expect from it, in terms of effort to learn? (Understandable or intuitive, ease to become skilfully, simple to use…) • Has it achieved it? Any critics? 4. Influence • Has someone influenced you? (Neighbours, important people, famous, testimonials regarding the benefits or a network of farmers…) • To what extent do their opinions influence your decision when you adopted this technology? Do you regret trusting on them? 5. Facilitating conditions • Did you feel ready to use the technology? (Other resources, knowledge, compatibility, assistance, training)? • Any critics regarding this? What you most or less like of it? 6. Price Value • Did you think the benefits of this technology justified its cost before buying it? • Has it achieved it? Any critics? 7. Hedonic motivation • Were you expecting it to be enjoyable, funny to use? A| Appendix: Interviews template 121 • Has it been? Has it been a problem? 8. Habit • Did you think this adoption would adapt well in your farming routine or disrupt it? • Has it been a problem or not? 9. Environmental Uncertainty • I imagine you agree with the fact that farming is a very evolving sector and everyday there are new external climatic, market, and policy changes. Has the technology helped you manage these uncertainties? • Has it been a problem or not? - About the 9 mentioned which were the important ones in the final decision? - Do you see yourself still using this technology in the long term? Which of the 9 mentioned items would be the one causing to stop using it? 122 A| Appendix: Interviews template Section 3: Focused on a Shared Model Here I have listed some problems that farmers can find in their job. I need you to say YES or NO in each. Say yes if it affects, concerns you or if you would like to improve it. Note: Now explain how a Shared model would improve the points he mentioned and other benefits… read the reasons from the following box: - Cost Problems o Investment o Operational costs (maintenance, consumables…) o access to financing - Time Problems (Manual processes slow down operations) - Problem to access Advanced Technology / Specialized Technology o Affordable innovations (Drones, Precision Farming Tools) o Newer versions - High risks (Machinery break) - Low scalability - Inefficiency utilization of resources (Tech not used frequently) - Not reaching Sustainability and Environmental goals o Less machines o Better one - Low market power o Better product o New network o Meet certification standards - Low flexibility o Rigid business structure - Low Community bonds A| Appendix: Interviews template 123 Note: extra explanations to make him understand the working if he the farmer is not familiar with Shared business models A shared business model is a system where multiple farmers collaborate to share resources, reduce costs, and increase efficiency rather than operating individually. Instead of each farmer - Cost Problems o Investment→ Farmers co-invest in machinery and infrastructure, reducing individual financial burdens o Operational costs → Shared ownership spreads maintenance and running costs among multiple users. o access to financing→ Group funding applications increase eligibility for loans, grants, and subsidies - Time Problems (Manual processes slow down operations) → Access to more efficient tools - Problem to access Advanced Technology / Specialized Technology o Affordable innovations (Drones, Precision Farming Tools) → Farmers share expensive tools, making advanced technology accessible. o Newer versions → Instead of buying outdated models, farmers upgrade through shared access to the latest technology - High risks (Machinery break) → Shared equipment might ensure backup options - Low scalability → Access to better tools that allows scaling up without requiring major personal investments - Inefficiency utilization of resources (Tech not used frequently) - Not reaching Sustainability and Environmental goals o Less machines → Farmers reduce redundant equipment o Better one→ Shared investment allows access to high-quality, - Low market power o Better product→ access machines that improve quality of your product o New network o Meet certification standards→ access to certificates that improve quality - Low flexibility o Rigid business structure→ Shared business models allow adaptive strategies - Low Community bonds→ strengthening trust, knowledge exchange 124 A| Appendix: Interviews template owning all their machinery, technology, land, or workforce, they co-own, rent, or access services collectively to improve productivity and market opportunities. Examples would be Equipment and Resource Sharing, Cooperatives, Land and Infrastructure Sharing, sharing seasonal workers or hiring specialized staff together, ✔ Lower costs – Reduces investment in expensive equipment and infrastructure. ✔ Increased efficiency – Maximizes the use of tools, land, and labour. ✔ Improved market access – Strengthens farmers’ bargaining power and access to certifications. ✔ Greater flexibility – Enables farmers to scale production up or down based on demand. ✔ Sustainability – Reduces environmental impact by minimizing redundant machines and optimizing resources. A| Appendix: Interviews template 125 TPB Focused questions 1. Attitude Toward the Behaviour • What is your overall opinion about using this business model? • Do you think it would bring benefits or challenges to your farm operations? 2. Subjective Norms • How do you think other farmers, family members, or advisors would feel about you using this business model? • Would their opinions influence your decision? 3. Perceived Behavioural Control • How confident are you in your ability to implement and sustain this business model, considering your current resources, knowledge, and time? • Do you believe you can overcome the barriers you face to adopt this business model? 4. Behavioural Intention • How likely are you to consider implementing this business model on your farm soon? What would motivate you to take that step, or what concerns might hold you back? Additional questions to address gaps 1. Comfort with Sharing • How many farmers would you feel comfortable sharing equipment or resources with? Would you prefer a small, trusted group or a larger network? • Would you prefer sharing only with farmers you already know, or would you be open to working with new collaborators? 2. Payment Preferences and Ownership • Which payment model would you find most suitable for participating in a shared business model? Fixed Membership Fee, Pay-Per-Use, Hybrid Model…? • Would you prefer to co-own the machine with other farmers or access the machine as a service provided by an external company or cooperative? 3. Scheduling and Use Coordination 126 A| Appendix: Interviews template • How do you think access to shared equipment should be scheduled? Should there be a booking system, a priority list, or a rotation? • How flexible would you be with adjusting your work schedule based on the availability of shared equipment? 4. Maintenance Preferences • Who do you think should be responsible for maintaining shared equipment? Should it be a shared responsibility, a rotating duty, or managed by an external service? 5. Trust and Conflict Resolution • What concerns would you have about trusted others to use shared equipment responsibly? • If disagreements arise over scheduling, payment, or maintenance, how do you think they should be resolved? 6. Data and Technology Use • Would you be comfortable using a digital platform to manage scheduling, payments, and equipment use? • Would you be willing to share data (e.g., usage patterns, maintenance needs) to improve the efficiency of a shared system? 7. Long-Term Commitment and Exit Strategies • Would you prefer a short-term trial period before committing to a shared model? • If you decide to leave the shared model, what exit options would you find fair (selling your share, transferring rights to another farmer, paying a previous accorded fee…)? 127 List of Figures Figure 2.1: Illustration of the Technology Acceptance Model (TAM). Source: (Miller and Khera 2010) ..................................................................................................................... 16 Figure 2.2: Illustration of the UTAUT. Source: (Omer et al. 2015) ................................. 18 Figure 2.3: Types of UTAUT Extensions. Source: (Venkatesh, Thong, and Xu 2016) . 19 Figure 2.4: Illustration of the TPB. Source: (Knauder and Koschmieder 2018) ............ 21 Figure 2.5: Detailed Illustration of TPB. Source: (Housman 2003) ................................. 22 Figure 4.1: UTAUT Template for Analysis. Source: Own elaboration .......................... 47 Figure 4.2: TPB template for Analysis. Source: (Knauder and Koschmieder 2018) ..... 47 Figure 6.1: Gabri’s individual UTAUT framework. Source: Own elaboration ............ 68 Figure 6.2: Gabri’s individual TPB framework. Source: Own elaboration ................... 69 Figure 6.3: Jordi’s individual UTAUT framework. Source: Own elaboration .............. 72 Figure 6.4: Jordi’s individual TPB framework. Source: Own elaboration..................... 74 Figure 6.5: Berta’s individual UTAUT framework. Source: Own elaboration ............. 77 Figure 6.6: Berta’s individual TPB framework. Source: Own elaboration .................... 78 Figure 6.7: Sancho’s individual UTAUT framework. Source: Own elaboration .......... 82 Figure 6.8: Jordi’s individual TPB framework. Source: Own elaboration..................... 83 Figure 6.9: Joan’s individual UTAUT framework. Source: Own elaboration .............. 86 Figure 6.10: Joan’s individual TPB framework. Source: Own elaboration ................... 87 Figure 6.11: Generalized UTAUT framework for small farmer’s Technology Adoption. Source: Own elaboration ...................................................................................................... 94 Figure 6.12: Generalized TPB framework for small farmer’s engagement in a Shared Business Model. Source: Own elaboration ........................................................................ 94 129 List of Tables Table 2.1: UTAUT’s concepts and definitions. Source: (Papagiannidis 2022) .............. 19 Table 4.1: Additional UTAUT concepts and their definitions. Source: Own elaboration .................................................................................................................................................. 46 Table 6.1: Construct connections and rating. Source: Own elaboration ........................ 91 Table 6.2. Moderator Grouping for UTAUT. Source: Own elaboration ........................ 92 Table 6.3: Drivers identified as key motivations for small farmers to adopt technology through a shared business model. Source: Own elaboration ......................................... 97 Table 6.4: Barriers identified as key challenges within shared business models for small farmers. Source: Own elaboration. ..................................................................................... 99 Table 6.5: Barriers identified as key challenges outside shared business models for small farmers. Source: Own elaboration. ......................................................................... 101