Sensemaking in the time of COVID-19

The COVID-19 pandemic has created an environment that is dynamically uncertain - routines are upended, normal interactions are disrupted, and risk must be reassessed on an ongoing basis. The pandemic offers the unique opportunity to study sensemaking within a context that is enormously complex, novel, and rapidly changing. At the same time, this pandemic brings to the foreground assumptions and questions about sensemaking theory that have remained largely unexamined. In a field often focused on corporate managers and elite first responders, these organizational actors are neither powerful nor sexy (from a research standpoint). This points to the need to revisit the alignment between where critical sensemaking is currently taking place and where we tend to study it. Sensemaking research can be enhanced by exploring it in a much wider range of organizations. (PsycInfo Database Record (c) 2023 APA, all rights reserved)

Acceptability of the Bicycle as Alternative Transport in the Financial Sector of Quito in the Context of the Covid 19 Pandemic

For a city to become resilient, smart measures must be taken that can cope with unexpected events such as the arrival of a pandemic or natural disasters caused by climate change and also prevent further destruction of our planet. Transport powered by combustion engines is one of the main emitters of greenhouse gases that increase the temperature of the planet and cause climate change. The public transport service of the city of Quito is the most used by its inhabitants and is mostly made up of combustion engine buses, however, during certain months of the years 2020 and 2021 it was suspended and the capacity was also limited to prevent the spread of the covid-19 virus, as it has become one of the main sources of contagion. The limitation of public transport led to economic, political and social instability. Some citizens and transport operators chose to break the law due to the limitations of public transport. Given the demand for transport, this study calibrated ten multinomial logit econometric models to estimate the probability of acceptance of the bicycle as an alternative mode of transport to public transport and taxis in the financial sector of Quito, using stated and revealed preference surveys. The bicycle is considered as a sustainable means of transport capable of solving the need to move around with social distance and in situations where public transport is limited. © 2022 IEEE.

Planning the Trauma Hospital Network Expansion to Increase Access in Rural Communities

Trauma care services are a vital part of all healthcare-based networks as timely accessibility is important for citizens. Trauma care access is even more relevant when unexpected events such as the COVID-19 pandemic overload the capacity of hospitals. Research literature has highlighted that access to trauma care is not equal for all populations, especially when comparing rural and urban groups. In this research we present a decision-making model for the expansion of a trauma hospital network by considering the demand for services of rural communities. The decision-making model provides recommendations in terms of where to place additional aeromedical facilities and where to locate additional trauma hospitals. A case study is presented for the state of Texas, where a sensitivity analysis was conducted to consider changes in demand, cost, and the total number of facilities allowed in the network. The results show that the location of new facilities is sensitive to the expected service demand and the maximum number of facilities allowed in the network. © 2022 IISE Annual Conference and Expo 2022. All rights reserved.

Machine Learning Techniques to Analyze Pandemic-Induced Economic Outliers

Outliers, or outlying observations, are values in data, which appear unusual. It is quite essential to analyze various unexpected events or anomalies in economic domain like sudden crash of stock market, mismatch between country’s per capita incomes and overall development, abrupt change in unemployment rate and steep falling of bank interest to find the insights for the benefit of humankind. These situations can arise due to several reasons, out of which pandemic is a major one. The present COVID-19 pandemic also disrupted the global economy largely as various countries faced various types of difficulties. This motivates the present researchers to identify a few such difficult areas in economic domain, arises due to the pandemic situation and identify the countries, which are affected most under each bucket. Two well-known machine-learning techniques DBSCAN (density based clustering approach) and Z-score (statistical technique) are utilized in this analysis. The results can be used as suggestive measures to the administrative bodies, which show the effectiveness of the study. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.