From intention to action: A predictive model for drone adoption towards sustainability among Iranian farmers
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The rapid advancement of drone technology presents significant opportunities for enhancing agricultural practices, yet the adoption of drones among farmers remains limited. Understanding the factors influencing drone adoption is crucial for extension of innovative agricultural practices towards sustainability. This study aims to explore the factors influencing drone adoption among farmers in Iran, using the Unified Theory of Acceptance and Use of Technology (UTAUT) model through evaluating the predictive power of this model in distinguishing between the drone adopters and non-adopters. A survey was conducted with a sample of 203 farmers due to the stratified random sampling, using a structured questionnaire for data collection. The validity and reliability of the questionnaire were confirmed through a panel of university professors and a pilot study assessing Cronbach's alpha. Data were analyzed using SPSS 26 , employing t-tests, ANOVA, and discriminant analysis. The results reveal significant differences between the two groups, highlighting that adopters exhibit stronger social influence, higher performance expectancy, and greater behavioral intention towards drone use. Experience, behavioral intention, and facilitating conditions are found to be key drivers for adoption, with younger and middle-aged farmers showing more intention to adopt drones. Furthermore, farmers engaged in non-agricultural activities exhibit higher adoption intentions, emphasizing the value of diversified income sources. The discriminant analysis based on the UTAUT model correctly classified 89.7% of the farmers, demonstrating its strong predictive power. This study underscores the importance of experience, education, and facilitating conditions in extension of drone adoption and offers policy recommendations for enhancing adoption rates, particularly through targeted interventions, financial support, and educational programs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it