Likert scales are a common methodological tool for data collection used in quantitative or mixed-method approaches in multiple domains. They are often employed in surveys or questionnaires, for benchmarking answers in the fields of disaster risk reduction, business continuity management, and organizational resilience. However, both scholars and practitioners may lack a simple scale of reference to assure consistency across disciplinary fields. This article introduces a simple-to-use rating tool that can be used for benchmarking responses in questionnaires, for example, for assessing disaster risk reduction, gaps in operational capacity, and organizational resilience. We aim, in particular, to support applications in contexts in which the target groups, due to cultural, social, or political reasons, may be unsuitable for in-depth analyses that use, for example, scales from 1 to 7 or from 1 to 10. This methodology is derived from the needs emerged in our recent fieldwork on interdisciplinary projects and from dialogue with the stakeholders involved. The output is a replicable scale from 0 to 3 presented in a table that includes category labels with qualitative attributes and descriptive equivalents to be used in the formulation of model answers. These include examples of levels of resilience, capacity, and gaps. They are connected to other tools that could be used for in-depth analysis. The advantage of our Likert scale-based response model is that it can be applied in a wide variety of disciplines, from social science to engineering.

Every year, natural hazards affect millions of people around the world, causing significant economic and life losses. The rapid progress of technology and advances in understanding of the highly complex physical phenomena related to various natural hazards have promoted the development of new disaster-mitigation tools, such as earthquake early warning (EEW) systems. However, there is a general lack of integration between the multi-and crossdisciplinary elements of EEW, limiting its effectiveness and applications for end users. This paper reviews the current state-of-the-art in EEW, exploring both the technical components (i.e., seismological and engineering) as well as the socio-organizational components (i.e., social science, policy, and management) of EEW systems. This includes a discussion of specific evidence from case studies of Italy, United States' West Coast, Japan, and Mexico, where EEW systems have reached varying levels of maturity. Our aim is to highlight necessary improvements for increasing the effectiveness of the technical aspects of EEW in terms of their implications on operational, political/legal, social, behavioral, and organizational drivers. Our analysis suggests open areas for research, associated with: 1) the information that needs to be included in EEW alerts to implement successful mitigation actions at both individual and organizational levels; 2) the need for response training to the community by official bodies, such as civil protection; 3) existing gaps in the attribution of accountability and development of liability policies involving EEW implementation; 4) the potential for EEW to increase seismic resilience of critical infrastructure and lifelines; 5) the need for strong organizational links with first responders and official EEW bodies; and 6) the lack of engineering-related (i.e., risk and resilience) metrics currently used to support decision making related to the triggering of alerts by various end users.

Earthquake early warning (EEW) is becoming a popular tool for mitigating earthquake-induced losses. However, the current literature separates the EEW's technical components and their operational and behavioural implications.This paper investigates how EEW can be integrated into business continuity practices, organisational resilience, and disaster risk reduction. We use a mixed-method approach to analyse EEW perceptions in the case study of Mexico City (Mexico), a city characterised by high seismic hazard, and social and physical exposure/vulnerability. Our dataset includes evidence from 15 semistructured interviews with representatives of the public and private sectors (e.g., governments, enterprises) and 78 valid questionnaires compiled by local organisations, including civil protection, education institutions, and enterprises.Our results reveal inconsistencies between technical EEW methodologies and their integration in three core domains of organisational practices: accountability, governance, and jurisdiction; standardisation of plans and procedures; training and education. Finally, we highlight open challenges for future research.

Regional earthquake early warning (EEW) alerts and related risk-mitigation actions are often triggered when the expected value of a ground-motion intensity measure (IM), computed from real-time magnitude and source location estimates, exceeds a predefined critical IM threshold. However, the shaking experienced in mid- to high-rise buildings may be significantly different from that on the ground, which could lead to sub-optimal decision-making (i.e., increased occurrences of false and missed EEW alarms) with the aforementioned strategy. This study facilitates an important advancement in EEW decision-support, by developing empirical models that directly relate earthquake source parameters to resulting approximate responses in multistory buildings. The proposed models can leverage real-time earthquake information provided by a regional EEW system, to provide rapid predictions of structure-specific engineering demand parameters that can be used to more accurately determine whether or not an alert is triggered. We use a simplified continuum building model consisting of a flexural/shear beam combination and vary its parameters to capture a wide range of deformation modes in different building types. We analyse the approximate responses for the building model variations, using Italian accelerometric data and corresponding source parameter information from 54 earthquakes. The resulting empirical prediction equations are incorporated in a real-time Bayesian framework that can be used for building-specific EEW applications, such as (1) early warning of floor-shaking sensed by occupants; and (2) elevator control. Finally, we demonstrate the improvement in EEW alert accuracy that can be achieved using the proposed models.