| Variable |
----------------------------Description---------------------------- |
---------------------Theoretical Foundation--------------------- |
| Adoption of integrated systems (y) |
A binary (dummy) variable, assuming a value of 1 if the farmer has adopted a Farm Management Information Systems (FMIS), and 0 otherwise. |
|
| Log_AGE (x1) |
Age of the farmer, expressed in years. |
The literature indicated that personal characteristics - such as age - play a decisive role in shaping technology adoption behavior. The inclusion of this variable is supported by empirical evidence from NIKKILÄ et al. (2010) and SORENSEN et al. (2010). |
| Education (x2) |
Binary (dummy) variable taking a value of 1 if the farmer holds high school education, and 0 otherwise. |
Higher levels of education enhance individuals’ cognitive capacity to assimilate technological innovations and are consistently found to be positively correlated with the adoption of agricultural technologies (LI et al., 2019). |
| Risk aversion (x3) |
A proxy variable for the farmer’s risk propensity. It is measured using a Likert scale based on the farmer’s level of agreement with the following statement: “When it comes to business, I prefer the safer option, even knowing that I may earn less”. The scale ranges from 1 (strongly disagree) to 5 (strongly agree). Higher values indicate a greater inclination toward risk aversion. |
Risk aversion represents a critical behavioral dimension in farmers’ decision-making processes. Its inclusion as an explanatory variable is justified by the findings of FOUNTAS et al. (2015), who emphasize the role of psychological and attitudinal factors in technology uptake. |
| Production (x4) |
Ordinal variable measured on a Likert scale ranging from 1 (not important) to 5 (very important), based on the following statement: “Improving yields and productivity is an important reason to adopt a FMIS” |
Motivational aspects - particularly those linked to operational efficiency and productivity improvement - emerge as key drivers of adoption. The conceptual grounding for these variable draws on the research of SOUZA & LOPES (2024), BIO (2008), and PADOVEZE (2009), which highlighted the economic rationale underlying adoption behavior. |
| Quality (x5) |
Ordinal variable measured on a Likert scale from 1 (not important) to 5 (very important), in response to the statement: “Improving product quality is an important reason to adopt a FMIS” |
Perceived value and expected benefits exert a direct influence on adoption intentions, whereas perceived risks tend to diminish such propensity (ZHANG et al., 2023). |
| Control (x6) |
Ordinal variable measured on a Likert scale from 1 (not important) to 5 (very important), referring to the following statement: “Improving fiscal and accounting control is an important reason to adopt a FMIS” |
Fiscal and accounting control constitutes a central motivation for the adoption of Farm Management Information Systems (FMIS). Studies by SOUZA & LOPES (2024), BIO (2008), and PADOVEZE (2009) emphasized that digital management tools contribute to greater transparency, traceability, and regulatory compliance within agricultural enterprises. |
| Capex (x7) |
Ordinal variable measured on a Likert scale from 1 (strongly disagree) to 5 (strongly agree), based on agreement with the statement: “A potential barrier to adopting a FMIS is the farm investment capacity” |
Implementation cost remains one of the most prominent barriers to technological adoption. The relevance of this constraint is substantiated by TEKINERDOGAN et al. (2019) and SMITH et al. (2019), who demonstrated that financial considerations often outweigh perceived long-term benefits. |
| Trust (x8) |
Ordinal variable measured on a Likert scale from 1 (strongly disagree) to 5 (strongly agree), derived from the statement: “A potential barrier to adopting a FMIS is the lack of trust in the recommendations, or the incompleteness of solutions offered to meet the farmer’s needs” |
Trust in technological systems and perceived risk exert a decisive influence on adoption outcomes. Greater trust fosters adoption, whereas elevated perceptions of risk act as deterrents (VERDOUW et al., 2021). |
| Labor (x9) |
Ordinal variable measured on a Likert scale from 1 (strongly disagree) to 5 (strongly agree), in relation to the statement: “A potential barrier to adopting a FMIS is the lack of skilled labor to operate the software/technologies” |
The shortage of workers equipped with the technical competencies necessary to operate digital technologies is widely acknowledged as a major impediment to adoption (MOYA et al., 2025). This structural limitation underscores the broader challenge of human capital development in digital agriculture. |
Adoption determinants, perceived benefits, and barriers to digital management systems in agriculture