An Evaluation of Community Adoption of the InaRISK BNPB Platform for Disaster Management: An Application of the Technology Acceptance Model (TAM)
Bibliographic record
Abstract
This study explores the community acceptance of the InaRISK BNPB platform, a novel approach to disaster management that integrates digital technology, Geographic Information Systems (GIS), and the Internet of Things (IoT).The Technology Acceptance Model (TAM) is utilized as a theoretical framework to decipher the acceptance patterns.Employing a quantitative research design, a survey methodology was adopted involving 47 participants, each over 18 years of age and having prior experience with the InaRISK BNPB platform.Data was collated from both primary and secondary sources.The primary data was gathered through questionnaires, while secondary data was obtained via an exhaustive literature review.The study implemented a quantitative descriptive analysis, alongside simple and multiple regression analyses for data interpretation.Findings suggest a significant impact of perceived ease of use on perceived usefulness, thereby influencing attitudes towards use and behavioral intentions to use the platform.Notably, attitude towards use was found to directly affect behavioral intention to use the platform.These findings underscore the salience of usability and intuitiveness in fostering technology acceptance.Consequently, it is imperative to enrich the features of InaRISK, making it not only more informative but also user-friendly, to bolster its adoption within the community.To augment the platform further, promoting transparency and information sharing across diverse sectors and stakeholders is deemed essential.This collaborative endeavor can enhance the quality and comprehensiveness of the information available on the InaRISK platform, thereby transforming it into an integrated disaster information hub.The potential contribution of this transformation to the advancement of digital IT-based disaster management is substantial.
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 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 | 0.003 | 0.000 |
| 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 | 0.001 | 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".