Extension Agents’ Use and Acceptance of Social Media: The Case of the Department of Agricultural Extension in Bangladesh
Bibliographic record
Abstract
Information and Communication Technologies (ICTs) have been considered as key driving forces for enabling agricultural development ‒ the sector which provides livelihoods for majority of the population in Bangladesh. The Department of Agricultural Extension (DAE), the largest public sector agricultural extension service provider in Bangladesh, has recently enacted a new organizational policy for its staffs to use ICTs such as social media to provide better services. However, there is little or merely anecdotal evidence about how extension agents of DAE have been accepting and using social media for their professional work. Drawing on the theoretical underpinnings of the Technology Acceptance Model (TAM), this study is a first attempt to investigate social media use and acceptance among extension agents in Bangladesh. Data was collected using semi-structured questionnaires from 140 extension agents of DAE who work in the eastern region of Bangladesh. Both descriptive and inferential statistics were used to analyze the data. The findings indicate that most extension agents (51.4%) used social media for half an hour to one hour every day. Perceived ease of use (PEoU) and Perceived usefulness (PU) are the most influential elements that determine DAE staff acceptance of social media for performing professional functions. Social media was perceived by extension agents as a means for improving professional performance, such as disseminating agricultural information; garnering support for new agricultural policy; networking with clients and colleagues and enabling coordination of services provided by colleagues. Overall, the findings indicate potential uses of social media in an ICT-based agricultural development strategy in Bangladesh.
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How this classification was reachedexpand
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".