The Role of Organizational Support and Behavioral Control in Shaping Visitors’ Willingness to Buy Innovations at Agricultural Shows in Uganda
Bibliographic record
Abstract
Agricultural shows provide a platform for showcasing research innovations, yet the factors influencing visitors’ willingness to buy these innovations remain unclear. This study examined the impact of organizational support, behavioral control, intrinsic and extrinsic motivation, and social norms on visitors’ buying intentions. Using Self-Determination Theory (SDT), the Theory of Planned Behavior (TPB), and Organizational Support Theory (OST), a structural equation modelling approach (SmartPLS 4) was applied to survey data from 304 farmers who had attended the National Agricultural Show at Jinja in 2024. Findings revealed that perceived organizational support (β = 0.621, p = 0.009) and behavioral control (β = 0.96, p = 0.008) were the strongest predictors of visitors’ willingness to buy exhibited innovations. In contrast, intrinsic motivation, attitude, and social norms were not significant predictors. Ability to use the innovations partially mediated the influence of organizational support on willingness to buy exhibited innovations. The R2 value showed a strongly prediction power of the model (R2 = 0.631; p = 0.001).It can be concluded that institutional engagement, systematic support via post-event training, and hands-on learning experiences are more effective in driving willingness to buy innovations at shows than motivation-based strategies. Policymakers and extension agents should prioritize structural and organizational factors when promoting evidence-based agricultural innovations via planned agricultural events.
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.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".