Human suffering has increased in recent years owing to increased intensity ad frequency of disasters. These are expected to further increase in the coming years due to climate change. Although natural disaster risks to humans cannot be completely eliminated, they can be minimized through efficient and effective humanitarian logistics (HL). Considering the importance of HL in reducing the impacts of disasters through fair distribution, this study aims to address the following question: “How can the performance, efficiency and effectiveness of HL be improved through transparency?” The primary data were collected through an online structured questionnaire from the employees participating in relief operations in Pakistan. This specific research model is reflective. Therefore, a covariance-based structure equation model (CB-SEM) based on confirmatory factor analysis (CFA) with SmartPLS software was used. The study tested the items’ reliability, discriminate validity, goodness of fit, and psychometrical soundness of the hypothesized model. The study results indicate that the relationship between predictor variables (disclosure, clarity, accuracy, corporate governance, decision making and accountability) and the response variable (effective HL) is mediated by public trust. Furthermore, the study suggests that public trust plays an imperative role in enhancing the performance, efficiency and effectiveness of HL. In addition, first, the study findings are expected to be beneficial for all stakeholders of disaster risk management, especially for governments, donors and humanitarian organizations (HOs), because they are persistently seeking strategies to assist victims. Second, most importantly, this study raises awareness of the need to carefully evaluate decisions related to the fair distribution of relief items. Third, the structure of this article reveals research gaps and promising areas for further research. This article provides a deeper understanding of transparency in HL using empirical data, which has not been explored before.
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