The role of organizational culture and voluntariness in the adoption of artificial intelligence for disaster relief operations
Bibliographic record
Abstract
Purpose The study explores the readiness of government agencies to adopt artificial intelligence (AI) to improve the efficiency of disaster relief operations (DRO). For understanding the behavior of state-level and national-level government agencies involved in DRO, this study grounds its theoretical arguments on the civic voluntarism model (CVM) and the unified theory of acceptance and use of technology (UTAUT). Design/methodology/approach We collected the primary data for this study from government agencies involved in DRO in India. To test the proposed theoretical model, we administered an online survey questionnaire to 184 government agency employees. To test the hypotheses, we employed partial least squares structural equation modeling (PLS-SEM). Findings Our findings confirm that resources (time, money and skills) significantly influence the behavioral intentions related to the adoption of AI tools for DRO. Additionally, we identified that the behavioral intentions positively translate into the actual adoption of AI tools. Research limitations/implications Our study provides a unique viewpoint suited to understand the context of the adoption of AI in a governmental context. Companies often strive to invest in state-of-the-art technologies, but it is important to understand how government bodies involved in DRO strategize to adopt AI to improve efficiency. Originality/value Our study offers a fresh perspective in understanding how the organizational culture and perspectives of government officials influence their inclinations to adopt AI for DRO. Additionally, it offers a multidimensional perspective by integrating the theoretical frameworks of CVM and UTAUT for a greater understanding of the adoption and deployment of AI tools with organizational culture and voluntariness as critical moderators.
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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 | 0.007 | 0.030 |
| 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 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".