| Issue |
BIO Web Conf.
Volume 216, 2026
The 6th Sustainability and Resilience of Coastal Management (SRCM 2025)
|
|
|---|---|---|
| Article Number | 12001 | |
| Number of page(s) | 19 | |
| Section | Disaster Risk Management | |
| DOI | https://doi.org/10.1051/bioconf/202621612001 | |
| Published online | 05 February 2026 | |
Determinants of Enhanced Trained Human Resources in Flood Disaster Risk Reduction (A Case Study in Sidomulyo Village, Lamongan)
Department of Urban and Regional Planning, Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia
* Corresponding author: This email address is being protected from spambots. You need JavaScript enabled to view it.
Abstract
In many developing regions, top-down infrastructural solutions fail to mitigate recurrent disasters, such as the annual Bengawan Jero flood in East Java, Indonesia, which causes estimated losses of IDR 29 billion (approximately USD 1.8 million). This failure underscores the critical need for community-level capacity, yet Sidomulyo Village remains a “cold spot” with a severe deficit in trained human resources. This research aims to identify the key determinants for enhancing these resources. Employing Partial Least Squares-Structural Equation Modelling (PLS-SEM), the roles of local institutions, social capital and human capital were assessed. The analysis shows that human capital has a strong, direct positive effect on trained human resources (β = 0.644, p value 0.000), while local institutions and social capital have no significant direct effect. However, the study uncovers a critical indirect path wherein social capital significantly impacts trained human resources by mediating the development of human capital (β = 0.343, p value 0.013). The clear conclusion is that effective strategies for enhancing trained human resources must focus on building human capital while using social capital as the fundamental supporting mechanism.
© The Authors, published by EDP Sciences, 2026
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.

