ABSTRACT
Purpose: The purpose of this study is to reveal the readiness of the employees in the banking sector in the Republic of North Macedonia to adapt to the reor-ganisation of working hours while at the same time using the safest payment methods in conditions when the world is trying to deal with the crisis caused by the COVID-19 virus. Need for the study: The world is rapidly moving towards increasing digitalisation, which is part of all spheres of human life. The outbreak of the COVID-19 virus pandemic has accelerated these processes by requiring people to adapt to the new conditions. The countries that have worked rapidly to digitise the sys-tem, while massively using non-cash payments, have adapted more easily to their regular daily tasks. The Republic of North Macedonia, as a developing country, is trying to take a step forward by introducing the innovations used by developed countries, taking into account the available assets and human resources. Methodology: A method for qualitative forecasting, Delphi, is used in three rounds, and the gained insights serve as inputs in the creation of two analytic hierarchy process (AHP) models. Findings: From the extensive analysis we performed, we found that the lack of digitalisation and process automation made it difficult for employees to adapt to the method of working from home, and on the other hand, they had a much easier time adapting to the use of alternative distribution channels. Practical implications: Our findings are useful for the country, regulatory bod-ies and the bank's management in developing strategies and plans for working from home or reorganisation of working hours, to be more acceptable to employees, emphasising the benefits for both employees and employers. Also, researchers and management practitioners in developing countries interested in this area can follow our combined Delphi-AHP approach in conducting similar research. © 2022 by Violeta Cvetkoska, Gokulananda Patel and Milanka Dimovska.
ABSTRACT
Identifying the interconnections among modules in a dynamic network from observed data poses a significant challenge in many scientific disciplines. Many methods for network reconstruction from observational data significantly limit the type of systems they are considering. For example, Granger causality considers only networks with strictly causal dynamics, and methods from the graphical models literature are focused on reconstructing networks with static relationships. In this article, we focus on a novel network reconstruction method, called Mixed-Delay (MD) that can consistently reconstruct a wide class of linear dynamic networks that do not contain any algebraic loops. However, the steps in the MD algorithm are of combinatorial complexity. In this article, we propose an optimization to the MD method that yields the method more informative and polynomial for sparse networks, while preserving the theoretical guarantees of the method. We demonstrate the optimized MD method on simulated and real data. The first real-data application aims to reconstruct networks that show the spread of COVID-19 in the US. Then we apply the method on monthly average temperature data and reconstruct temperature relationships among states in the US, as well as European and South-East Asian countries. Copyright (C) 2021 The Authors.
ABSTRACT
BACKGROUND: Faced with the novel coronavirus outbreak (2019-nCoV), various urgent and coordinated actions have been taken worldwide to reduce spread of the disease. Slowing down economic activities, transportation, restrictions of the human public gatherings, and interaction resulted with a tremendous decline in air pollutant concentration especially in nitrogen dioxide, registered by National Aeronautics and Space Administration and European Space Agency satellites. AIM: The aim of the study was to assess the impact of COVID-19 lockdown conditions on the air quality in selected cities in Macedonia. METHODS: Daily mean concentration of the particulate matter (PM10 and PM2.5 ), nitrogen dioxide, ozone, and carbon monoxide measured in the national air quality monitoring network, was analyzed separately comparing following periods: past week of February 2020 to the end of May 2020 with the same period in 2017–2019. Depending on the data distribution, parametric independent-samples t-test or nonparametric Mann–Whitney U-test was run to determine if there were differences in the pollutants concentration during the COVID-19 and non-COVID-19 period. RESULTS: Implementation of strict restrictions of the movement along with reduced economic activities and vehicular transport, led to notable decrement of air pollutant concentrations. We have found an evident decrease in the concentration levels of all pollutants measured during COVID-19 period in 2020, compared to those from 2017 to 2019 with exceptions for PM2.5 in Kumanovo and carbon monoxide in Skopje (7% and 3% higher concentration). The most notable decrement was for NO2, with a concentration 5–31% lower during COVID-19 period. CONCLUSIONS: Although beneficial to human health, there is a need to assess economic implications of the lockdown that could have a negative impact on the health as well.