Digital signatures have been widely and primarily used for document approval activities during the Coronavirus pandemic in Indonesia. This is the digital equivalent of a handwritten signature or stamped seal, although it provides more inherent security, such as validating the authenticity and integrity of a message, software, or virtual document. Therefore, this study aims to determine factors affecting consumer intention in using digital signatures based on (i) the unified theory of acceptance and use of technology 2, (ii) the theory of planned behavior, and (iii) the information acceptance model. A total of 358 respondents answered the online questionnaire containing 69 question items, with the data analyzed using the structural equation modeling technique to examine the hypotheses. The results showed that the relationship between consumers’ attitudes, perceived behavioral control, subjective norms, and information adoption had the highest and lowest effects on consumers’ behavioral intention. Moreover, the consumers’ attitudes had the most significant effect on their attitudinal intention to use digital signatures. The significant positive impact of these consumers’ attitudes had relevant implications for the sustainable adoption of the signature system. This indicated that an integrating model with the potential of extending to consumers’ intention analysis was established for digital signature adoption in other countries after the post-Coronavirus period.

The demand for humanitarian supply chains grows daily as the incidence of calamities rises. Typhoons cause thousands of casualties each year. As a result, policymakers and governmental authorities must develop effective readiness and response measures as part of pre-disaster plans. This paper proposed a stochastic model for multi-objective location-routing for creating a humanitarian network for pre-disaster response. The model aims to minimize the overall costs of the network’s setup, the time required to travel through it, and the number of vehicles necessary for transferring affected individuals to evacuation centers. The model concentrates on pre-disaster scenarios in uncertainty. The provided model was implemented in an actual scenario in one of the Philippines’ provinces and solved using Multi-Objective Particle Swarm Optimization (MOPSO), which is also contrasted with Multi-Objective Simulated Annealing (MOSA) and the ε-constraint approach. According to empirical findings, the model can be used to identify distribution hubs and evacuation centers and choose the best routes in unexpected and actual disaster scenarios. Given that the ideal number, location, and capacity of DCs and ECs are known in advance, government decision-makers can solve any potential shortages and problems during the disaster.