Integration of Internet-of-Things With Blockchain Technology to Enhance Humanitarian Logistics Performance

Abstract

There has been incredible interest in Internet-of-Things (IoT) and blockchain technology (BCT) around the world and across sectors. Following great achievement in the other sectors, the implementation of IoT and BCT have gained great interest in Humanitarian Logistics (HL) at many levels despite remaining in an earlier stage. The profit and non-profit organizations both are under increasing worldwide pressure for transparency, with donors and governments calling for enhanced transparency and information exchange in the humanitarian sector. This study, which is based on transactive memory systems (TMS) theory perspectives, proposes a study framework to understand “how can the transparency, public trust, and coordination in HL be improved through the integration of IoT with BCT?”. We framed and tested six research hypotheses, using data collected from Humanitarian Organizations (HOs) employees. We have applied a Covariance-based structure equation model (CB-SEM) with confirmatory factor analysis (CFA). This study results confirm that our all hypotheses were supported. The research results show that the association between explanatory variables (i.e., IoT and BCT) and the response variables (i.e., public trust and coordination) is mediated by transparency. This study provides substantial and valid contributions to the literature on IoT, BCT, transparency, public trust and coordination. This study proves that transparency plays a crucial role in enhancing public trust, coordination, and ultimately HL performance through the integration of IoT with BCT. The study results could be helpful for all the stakeholders of disaster risk management since they are insistently looking for strategies to support afflicts. Our study is a good candidate solution to raise awareness of fast, fair, and safe HL to reveal research gaps and provide opportunities for future research. The study will provide an enormous understanding of IoT and BCT in HL, which has not been investigated empirically before.