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This paper investigates the role of Islamic faith‐based organisations (FBOs) in Indonesia and examines the way in which their disaster recovery aid can be successful or less successful depending on social capital formation in communities affected by a disaster. The paper argues that Islamic FBOs play a prominent role in disaster‐affected communities by building new social capital or strengthening existing social capital. Failure to do so may affect a community's recovery and its long‐term resilience. Applying a framework that considers three types of social capital—bonding, bridging, and linking—from a comparative perspective, the paper discusses two cases of disaster recovery: one following the earthquake that struck Aceh in 2013; and the other after the Mount Kelud volcanic eruptions in East Java in 2014. In both instances, the findings highlight the importance of the village facilitator, cultural sensitivity, and understanding of local indigenous and religious practices for successful disaster recovery. [ABSTRACT FROM AUTHOR]

Smooth interaction with a disaster‐affected community can create and strengthen its social capital, leading to greater effectiveness in the provision of successful post‐disaster recovery aid. To understand the relationship between the types of interaction, the strength of social capital generated, and the provision of successful post‐disaster recovery aid, intricate ethnographic qualitative research is required, but it is likely to remain illustrative because it is based, at least to some degree, on the researcher's intuition. This paper thus offers an innovative research method employing a quantitative artificial intelligence (AI)‐based language model, which allows researchers to re‐examine data, thereby validating the findings of the qualitative research, and to glean additional insights that might otherwise have been missed. This paper argues that well‐connected personnel and religiously‐based communal activities help to enhance social capital by bonding within a community and linking to outside agencies and that mixed methods, based on the AI‐based language model, effectively strengthen text‐based qualitative research. [ABSTRACT FROM AUTHOR]