Our work takes into account a purposive sample of 23 farmers, and the application of the QM has allowed us to identify prevailing discourses whose interpretation contributes to enriching the debate on PF, providing a new perspective on the subject to policy makers. Additionally, the QM can be used to rethink policies for the dissemination of innovative tools, and, in this regard, provide a better understanding of the transfer of innovation to the agricultural sector to improve the effectiveness of innovation policy. Finally, the discourses of this study can provide new insights to boost responsible policies, even more so since RRI applications to PF are quite limited. The article consists of an introduction, followed by the theoretical background in which the transfer of technology for innovation processes is explored. Then, the key dimensions influencing PFT adoption are discussed, including the sphere of the self. In the third section, the methodology is presented, followed by the results. The discussion and conclusions close the work. In the period of agricultural modernization, innovation has been conceptualised as a linear and unidirectional flow of knowledge of a top down type from researchers to farmers. During the 1960s, the innovation process shifted from a “science push” model to a “market pull” model, underlining the role of demand . These approaches, defined as technology oriented, aim to study the innovation process only through technical and economic factors . Over time, these trajectories, strongly disconnected from the needs of farmers and from the context in which innovation operated, led to explorations of more systemic approaches to innovation, such as the agricultural knowledge and information system and, later, mobile grow rack the agricultural innovation system .
In fact, it was only in the 1990s that innovation was conceptualised as a contextualised “networking process”, implying a learning process between actors. It is precisely this new conceptualisation that marked a change from “topdown” to “bottom-up” approaches, where science and technology are embedded within a social and institutional context . The contextual inclusion of “innovation processes”, well explained by Elzen et al. with the term “anchoring of innovation”, has been highlighted for adoption in the PF field . The literature has shown how anchoring mechanisms are an optimal strategy fostering an environment that is conducive to scaling innovation in this field . On the one hand, this evolution reflects the complexity of anchoring innovation processes in agricultural systems; on the other hand, it reflects that farmers’ thinking has played an increasingly active role in innovation processes over time . Hence, there are numerous contributions that researchers have proposed to try to identify the drivers of and barriers to adoption at the farm level. Even though governments, industry and funding agencies have made efforts to persuade farmers of the benefits of PF, adoption has been low or fragmented. Together with the analysis deepening the complexity of the transfer of innovation, researchers have tried to assess the reasons for this low uptake. First, numerous studies have tried to determine the characteristics of adopters and the contextual factors based on which farmers may more easily accept a new technology in their management . Most studies have pointed out that young farmers appear to be more involved in agricultural innovation . The reasons for this propensity lie in the fact that new generations report a higher level of education and, at the same time, a growing need for information, which is similarly positively correlated with adoption, in addition to greater exposure to and familiarity with virtual technologies .
The need to acquire skills in the use of these tools is also combined with the high investment cost of these tools. In fact, with their ability to absorb costs, large farms have been described as being more willing to adopt innovation. Small enterprises can become PFT adopters through contractors or partnerships . At the same time, the labour intensity indicator gives a clearer idea, in relation to the production activity analysed, of how much agricultural activities are accompanied by new tools or whether manual labour is still present.The role of adopters in the context in which innovation operates has been widely investigated in the literature by identifying numerous dimensions concerning not only the structural dimension and farmer perceptions but also the institutional context . In particular, the institutional context includes social and cultural dynamics and environmental and policy aspects . To understand adopters, researchers have explained how the decision to adopt is only partly linked to the structural and institutional dimensions of farms . Among the factors already mentioned, some studies also include the perceptions of farmers. Perception is the result of a subjective assessment made by the potential user regarding the attributes of innovation and the influences exerted by the structural and institutional dimensions in orienting behaviour in the adoption process . Among the attributes, many authors have focused on the perceived relative advantage and, in particular, farmers’ profitability . Others have highlighted that the perception of the technological and organisational complexity of innovation can significantly influence adoption . Many theories have tried to explain behaviour in the adoption process by emphasising the role of perceptions, the figure of the adopter and background factors .
Since the 1960s, the early theories and models of technology acceptance and adoption have emphasised the role of behaviour and perception as key variables in the adoption process. Fishbein and Ajzen’s theory of planned behaviour and later extensions postulated that the individual’s behaviour is the result of multiple components, such as attitude, subjective norms, and perceived behavioural control. In social cognitive theory , Bandura reports how behaviour, personal factors , and the external environment of the individual are bidirectionally connected in understanding the adoption process. Davis theorised differently in his technology acceptance model that attitudes are the determinants of behavioural intentions to perform an action or not and are based on perceived ease of use and perceived utility . The TAM itself has been extended by exploring the determinants of perceived utility and perceived ease of use, introducing the relationship between them into the structural dimension . These theories are the starting point and lay the groundwork for investigating the links between i) contextual and structural factors, ii) perceptions, and iii) behaviour that could predispose individuals to adopt new technologies. However, in these models, where perceptions or behaviour is taken into consideration, the agent is always considered rational . This is the vision offered by classical economics, in which the actor manifests autonomous and fixed preferences disconnected from the context . In contrast, in behavioural economics or in the field of sociology, researchers have spoken of “quasi-rational actors” and even “enculturated” decision makers, whose perceptions and behaviours are shaped by the context . The perception-behaviour link has been widely recognised in the psychological research field, which addresses how “perceptions guide action but so too do actions influence what is perceived” . The role of the self in this linkage has been highlighted by Jaswal , affirming that “perception-action coupling is not only manifest in the behavioural arena, but also shows up in the internal processes of the agents, particularly those related to the self”. This is confirmed by Markus and Kitayama , who discuss a mutual and dynamic constitution of context and the self. For example, regarding the concerns of the self, perceptions are subjected to profound social and non-social influences exerting lasting effects on the behaviour and in the moment of decision making due to the context to which individuals have been exposed until that moment .
Reimer et al. is one of the few studies that in the field of adoption that analyses how the characteristics of farmers and farms as well as the and farm context can shape the perception of a new technology and, consequently, the individual’s behavioural intentions towards it. The literature shows the enormous efforts made, especially regarding three aspects: codifying the phases of technology diffusion, theorising adoption models, and identifying the major drivers of and barriers to adoption and all its influencing factors. It is possible to summarise the points previously discussed as follows. The QM that we employed in this work is based on the five-step procedure shown by McKeown and Thomas . The five steps are outlined in Fig. 2. To carry out this analysis, the first two steps are the most important; defining the “concourse” and creating the Q set can affect the whole analysis. The former is the raw material of the Q study that provides the “self-referent notions” arising from shared understanding, whose specific meaning may differ depending on the context . Since the volume of the concourse can be infinite, it has to be dimensionally reduced to obtain the Q set, which is the collection of statements related to the most important aspects of the study theme . The sentences included here should represent a variety of different opinions and feelings rather than being limited to concrete facts . Following the procedure shown by McKeown and Thomas , the concourse was built using scientific publications, newspapers, farmer blogs or interviews, conversations, commentaries, and texts related to the subject. From this review, we defined a final concourse composed of 80 statements . Using an inductive approach , the analysis shows that several dimensions influence farmers’ perceptions of PF and its adoption. These dimensions are not a strict categorisation; rather, they represent a guide to ensure coverage of the most relevant aspects related to farmers’ opinions on PF. Several rounds of discussion were implemented among researchers to delete and rephrase redundant and unclear statements. At the end of the described procedure,ebb and flow table the initial list was refined into a more comprehensive Q sample composed of 33 statements. Q samples must be composed of statements that are “natural” in the language of the participants and “comprehensive” in their representation of the subject to provide individuals with the opportunity to best express their personal opinions . Consequently, the use of academic language should be avoided to facilitate understanding, and a balanced number of positive and negative statements should be included to avoid opposites or similar statements .
Small sheets of paper are used to print the declarations, which are also identified with a code that cannot influence the participant during the process. Before being administered to the sample, the test was tested by a collaborator. In our case, the list of declarations was chosen based on the literature on precision agricultural tool adoption, focusing on drivers and barriers. In the third phase, it is necessary to select participants who are theoretically relevant to the research question and who have a defined perspective to express what matters in relation to the topic . This interview method was first tested among the members of the research group to determine the best way to submit the questionnaire. After a test, it was decided to proceed from the socio-demographic questions and then proceed to the Q sorting phase. The P set is usually smaller than the Q sample, typically from 10 to 40 people . The reason for this can be found in an ancient maxim attributed to Roman Emperor Marcus Aurelius, who stated that “the opinion of 10,000 men has no value if none of them knows anything about the topic”, leading us to the choice of a purposive sample of farmers who have at least “heard of” PF . Therefore, an intentional sample of 23 farmers was selected. The interviews were conducted by two researchers who selected the respondents based on the question “Have you ever heard of innovation, technological innovation, or precision farming in agriculture?” This allowed us to select only those agricultural entrepreneurs who had the necessary conditions to carry out our questionnaire. The interviewees were asked to voluntarily participate in the study. No financial compensation was promised or subsequently awarded. They were informed of the objectives of the investigation, the duration of the interview, and the possibility of abandoning the investigation at any time, and they were given the contact details of the principal investigator for any clarifications or indications on the matter.