The Effect of Social Network on Accptability of New Technology in Developing Countries: A Case Study of Piped Water Adoption in Rural India
Bibliographic record
Abstract
It has been pointed out that new technologies introduced in developing countries have not been accepted by local residents in some cases. It is assumed that new technologies need to be adapted to the local social structure if the aim is to generate sustainable technology acceptance. In this paper, the effect of social network on acceptability of new technologies is examined through a case study on piped water adoption in rural India. Social Network Analysis is used to investigate how closed social network groups and the centrality of some individuals in social network affect technology acceptance of residents. The effect of these attributes on technology acceptance is examined using logistic regression model. Our results show 3 main findings as follows: (1) there are no similarities of piped water use among residents belong to the same closed social network group, (2) central persons who affect other residents’ technology use do not have high social status and play any role as a leader, thus, it is needed not to easily select persons who seem to be outstanding as key persons of technology adoption, (3) it is important to focus on not only individual attributes but also social network when new technologies are adapted.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".