Scalable Information Systems for Agribusiness: Developing Farmers’ Digital Capabilities for E-commerce Platform Adoption
Bibliographic record
Abstract
INTRODUCTION: Digital transformation is considered as challenging yet imperative in Vietnam recently. In agriculture sector, one of the directions to comprehensively pro-mote digital transformation is to encourage and support farmers to promote their agribusiness on e-commerce platforms.OBJECTIVES: This study aims to exploratorily develop a framework for farmers’ digital capabilities for e-commerce agribusines and empirically examine how the dimensions of such developed framework impact farmers’ adoption of e-commerce platforms for promoting their agriculture products.METHODS: A mixed method study design is employed. We conduct a literature review of recognized databases and focus group technique to develop a framework for farmers’ digital capabilities for e-commerce agribusiness. Moreover, a field survey is designed to collect empirical data of farmers’ perceptions on adopting e-commerce agribusiness and quantitatively determine how dimensions of farmers’ digital capabilities could impact their adoption of e-commerce platforms. EFA (Exploratory Factor Analysis) and multiple regression are used for data analysis. RESULTS: Study findings show that the four dimensions of farmers’ digital capabilities for e-commerce agribusiness (Attitude toward e-commerce agribusiness, Basic ICT capabilities, E-commerce digital marketing capabilities, and Digital learning capabilities) positively contribute to their adoption of e-commerce platforms.CONCLUSION: This study proposes a framework for farmers’ digital capabilities and verifies that the four dimensions of the framework could significantly enhance farmers’ e-commerce platform adoption. We recommend several practical means to boost farmers’ adoption. Future research could apply our proposed framework to examine the formation of farmers’ e-commerce adoption in social platforms and offer solutions to enhanced agribusiness.
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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".