Estimated the Willingness to Pay Levels for the Adoption of the Internet of Things-IoTs Technology: An Empirical Study in Swiftlet Farming in Binh Thuan Province, Vietnam
Bibliographic record
Abstract
Imported into Vietnam since 2005, domestic swiftlet farming has been being received, noticed, and invested by various stakeholders and multi-local government levels. Entering the digital era, digital technologies are amending the farming of swiftlet and the application of the Internet of Things-IoT technology along with Artificial Intelligence-AI is expanding rapidly. IoTs and Artificial Intelligent positively support farmers in order to collect, synthesize and analyze statistics of data, to be able to self-control, adjust the behavior of the farming activities based on precisely dosed indicators to limit the potential risks due to the enemies of the swiftlet or the bad guys stealing bird's nest. This study investigated 120 producers in Phan Thiet, Ham Thuan Bac, and Bac Binh where the highest population of swiftlet activity in Binh Thuan province, Vietnam. The study applies the Willingness to Pay method in combining with the Linear Regression Model (LRS) to estimate the level of the Willingness to Pay (WTP) and its determinants for the adoption of the Internet of Things (IoTs) technology. The findings indicated that producers agreed to pay 380 million VND (nearly 30% of total investment in equipment and technology) but the level of the WTP showed a large variation: 55 million of lowest group and while more than 1200 million of the highest one. The LRS model with 12 explanatory variables allowed to explain 51% of the factors' influence on the WTP. The findings indicated that should be taken into account the multi-aspects of solutions from producers, enterprises and local government achieve sustainable development in swiftlet farming.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".