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The research subject of this paper is the analysis of the attitudes of employees in pharmaceutical companies towards the business aspects of the pharmaceutical industry during and after the end of the pandemic in the Republic of Serbia. The aim is to examine the differences in the attitudes of employees, as well as to determine which variables predict the situations of endangering the professional reputation of pharmaceutical companies during the COVID-19 pandemic. The research was conducted by means of a survey during 2021 on a sample of 27 innovative and generic pharmaceutical companies. We used the SPSS program for descriptive statistics analysis, chi square test and binary logistic regression models. The findings show that there is a statistically significant difference in the expressed attitudes of employees in innovative and generic pharmaceutical companies in terms of coming to the office during the pandemic;the lack of medicines and medical devices used in the treatment of COVID-19 infections;the patient access to a chosen doctor;the expectations of the employees to continue working from home after the outbreak of the COVID-19 pandemic. The findings of the binary regression models show the slowdown in the supply chain, the access to doctors and working from the home office have not been perceived as creating situations of endangering professional reputations, that is, they contribute to the sustainable economic success. On the other hand, the introduction of digital technologies decreases the occurrence of conditions in which their professional reputation has been threatened.
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This study investigates how the course format change caused by covid-19 pandemic affected learning behaviors and performance of college students enrolled in a large introductory history course. Clickstream log files capturing how students were interacting with online learning contents were analyzed to identify the learning behaviors of students before and after the mid-semester course format change. The non-parametric regression model was developed to examine the relationship between learning performance of students and course format change. Although the frequency of accessing learning resources decreased during the first three weeks after the course format change, it had a relatively small effect on the learning performance of students. The quantile regression model indicates the mid-semester course format change is associated with about 3.3% decrease in the learning performance of students. These results suggest that students were quite resilient and their learning during the pandemic was not as bad as we feared.
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PurposeThis study explores Greek and Swedish consumers' attitude towards organic food consumption in order to demonstrate possible differences that can be identified based on health and ecological consciousness beliefs rather than demographic factors. The examination of an emerging and a more mature market allow the authors to provide more targeted marketing strategies that possibly increase organic food consumption in both countries.Design/methodology/approachThe authors adopt an econometric approach to the analysis of consumer behavior in relation to organic food consumption in Sweden and Greece. More specifically, the authors examine the motivations and postexperiences of organic food consumers of different socioeconomic profiles in these two countries, one in northern and one in southern Europe. The authors apply an ordered logistic regression analysis model to map out the interaction between consumer attitudes and sociodemographic variables.FindingsThe authors results show that consumers in Sweden more frequently purchase organic foods than consumers in Greece. Environmental protection and ethical values increase the odds for Swedish organic food consumers to buy organic food products. Health consciousness and family well-being are perceived as factors that increase the odds for Greek organic food consumers to buy organic foods. Sociodemographic factors do not play a pivotal role for consumer behavior in relation to organic food in both countries.Originality/valueThis study distinguishes between organic food consumers in two countries with different levels of organic food production and export activity, size of organic market, national organic labeling system and legal definition and standards of organic food. Within these differences, the organic food industry could align its marketing efforts better rather focus on simplistic demographics. The current view unfolds the fact that there are limited studies comparing two European markets at different stages of development and the factors that influence organic food consumer behavior.
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The COVID-19 pandemic dramatically affected public transit systems around the globe. Because transit systems typically move many people closely together on buses and trains, public health guidance demanded that riders should keep a distance of about two meters to others changed the definition of "crowding” on transit in 2020. Accordingly, this research examines how U.S. public transit agencies responded to public health guidance that directly conflicted with their business model. To do this, we examined published crowding standards before the COVID-19 pandemic for a representative sample of 200 transit systems, including whether they started or changed their published standards during the pandemic, as well as the reasons whether agencies publicize such standards at all. We present both descriptive statistics and regression model results to shed light on the factors associated with agency crowding standards. We find that 56% of the agencies surveyed published crowding standards before the pandemic, while only 46% published COVID-19-specific crowding standards. Regression analyses suggest that larger agencies were more likely to publish crowding standards before and during the COVID-19 pandemic, likely because they are more apt to experience crowding. Pandemic-specific crowding standards, by contrast, were associated with a more complex set of factors. We conclude that the relative lack of pandemic standards reflects the uncertainty and fluidity of the public health crisis, inconsistent and at times conflicting with the guidance from public health officials, and, in the U.S., a lack national or transit industry consensus on appropriate crowding standards during the first year of the pandemic.
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El efecto disruptivo de la COVID-19 en la industria turística ha generado nuevas necesidades y motivaciones en los viajes turísticos. Este estudio evalúa los efectos de la pandemia en el perfil y características de los viajes de ocio realizados por los residentes en España. A partir de los microdatos de la Encuesta de Turismo de Residentes del INE, se utiliza el modelo de regresión logística para examinar la relación entre el perfil socioeconómico y demográfico del viajero y las características del viaje con el tipo de destino (internacional o doméstico) para los años 2019-2021. La comparación de los resultados estimados en cada año revela que la motivación del viaje al extranjero difiere significativamente entre los diferentes perfiles de turistas, excepto por razón de género. Los resultados también constatan que las características del viaje fueron significativamente diferentes antes y después de la pandemia. Además, se confirma un cambio significativo en las preferencias del tipo de alojamiento y transporte, junto con una reducción de las diferencias de duración de los viajes a destinos nacionales e internacionales.Alternate :The disruptive effect of COVID-19 pandemic on the tourism industry has generated new needs and motivations for tourist travel. This study evaluates the effects of the pandemic on the profile and characteristics of leisure trips made by residents in Spain. Based on the microdata from the Resident Tourism Survey carried out by the INE, the logistic regression model is used to examined the relationship between the socioeconomic and demographic profile of the traveler and the characteristics of the trip with the type of destination (international or domestic) for the years 20192021. The comparison of the estimated results for each year reveals that the motivation to travel abroad differs significantly between the different profiles of tourists, except for gender. The results also confirm that the characteristics of the trip were significantly different before and after the pandemic. In addition, a significant change in the preferences of the type of accommodation and transport is confirmed, along with a reduction in the differences in duration of trips to national and international destinations.
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The impact of the COVID pandemic has resulted in many people cultivating a remote working culture and increasing building energy use. A reduction in the energy use of heating, ventilation, and air-conditioning (HVAC) systems is necessary for decreasing the energy use in buildings. The refrigerant charge of a heat pump greatly affects its energy use. However, refrigerant leakage causes a significant increase in the energy use of HVAC systems. The development of refrigerant charge fault detection models is, therefore, important to prevent unwarranted energy consumption and CO2 emissions in heat pumps. This paper examines refrigerant charge faults and their effect on a variable speed heat pump and the most accurate method between a multiple linear regression and multilayer perceptron model to use in detecting the refrigerant charge fault using the discharge temperature of the compressor, outdoor entering water temperature and compressor speed as inputs, and refrigerant charge as the output. The COP of the heat pump decreased when it was not operating at the optimum refrigerant charge, while an increase in compressor speed compensated for the degradation in the capacity during refrigerant leakage. Furthermore, the multilayer perception was found to have a higher prediction accuracy of the refrigerant charge fault with a mean square error of ± 3.7%, while the multiple linear regression model had a mean square error of ± 4.5%. The study also found that the multilayer perception model requires 7 neurons in the hidden layer to make viable predictions on any subsequent test sets fed into it under similar experimental conditions and parameters of the heat pump used in this study.
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In chemistry and medical sciences, it is essential to study the chemical, biological, clinical, and therapeutic aspects of pharmaceuticals. To save time and money, mathematical chemistry focuses on topological indices used in quantitative structure-property relationship (QSPR) models to predict the properties of chemical structures. The COVID-19 pandemic is widely recognized as the greatest life-threatening crisis facing modern medicine. Scientists have tested various antiviral drugs available to treat COVID-19 disease, and some have found that they help get rid of this viral infection. Antiviral drugs such as Arbidol, chloroquine, hydroxychloroquine, lopinavir, remdesivir, ritonavir, thalidomide, and theaflavin are used to treat COVID-19. In this paper, reformulated leap Zagreb indices are introduced. Then, the reformulated leap Zagreb indices, leap eccentric connectivity indices, and reformulated Zagreb connectivity indices of these antiviral drugs are calculated. Curvilinear and multilinear regression models predicting the physicochemical properties of these antiviral drugs in terms of proposed indices are obtained and analyzed. The findings and models of this study will shed light on new drug discoveries for the treatment of COVID-19.
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The motivation for study derives from the requirements imposed by the European Union Corporate Sustainability Reporting Directive, which increases the sustainability reporting scope and the need for companies to use emerging digital technologies. The research aim is to evaluate the digital transformation impact of the European Union companies on sustainability reporting expressed through three sustainable performance indicators (economic, social, and ecological) based on a conceptual model. The data were collected from Eurostat for 2011–2021. The study proposes a framework for sustainable performance analysis through linear regression models and structural equations. Additionally, a hierarchy of digitization indicators is created by modeling structural equations, depending on their impact on sustainability performance indicators, which is validated using neural networks. The results indicate that the company's digital transformation indicators positively influence economic and social performance and lead to an improved environmental protection (a decrease in pollution), proving the established hypotheses' validity. The proposed model can be the basis for companies to create their dashboards for analyzing and monitoring sustainable performance. This research can be the basis of other studies, having a significant role in establishing economic and environmental strategies to stimulate an increase of companies that carry out sustainability reporting.
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Under the development pattern of the "double cycle”, optimizing urban economic resilience is tremendously meaningful to improving a city's affordability and the adaptability of the economy and to promoting the Chinese economy to develop with high quality. Based on Baidu migration big data perspective, exploratory spatial data analysis (ESDA) and multi-scale geographical weighted regression (MGWR) model were used to analyze the spatial characteristics and driving factors of economic resilience in 287 Chinese cities in 2019. The results show that (1) the number of low-level economically resilient cities is the largest and distributed continuously, while the number of high-level economically resilient cities is the lowest and distributed in clusters and blocks;(2) compared with the Pearl River Delta and Yangtze River Delta, the population accumulation characteristic of the Beijing- Tianjin-Hebei region is relatively slow;(3) Both net inflow of population after spring festival and daily flow scale are significantly correlated with urban economic resilience, and the former will affect urban economic resilience;and (4) the spatial heterogeneity of each factor driving is significant, and they have different impact scales. The impact intensity is as follows: net population inflow > innovation ability > public financial expenditure > financial efficiency > urban size.
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The moisture levels in sausages that were stored for 16 days and added with different concentrations of orange extracts to a modification solution were assessed using response surface methodology (RSM). Among the 32 treatment matrixes, treatment 10 presented a higher moisture content than that of treatment 19. Spectral pre-treatments were employed to enhance the model's robustness. The raw and pre-processed spectral data, as well as moisture content, were fitted to a regression model. The RSM outcomes showed that the interactive effects of [soy lecithin concentration] × [soy oil concentration] and [soy oil concentration] × [orange extract addition] on moisture were significant (p < 0.05), resulting in an R2 value of 78.28% derived from a second-order polynomial model. Hesperidin was identified as the primary component of the orange extracts using high-performance liquid chromatography (HPLC). The PLSR model developed from reflectance data after normalization and 1st derivation pre-treatment showed a higher coefficient of determination in the calibration set (0.7157) than the untreated data (0.2602). Furthermore, the selection of nine key wavelengths (405, 445, 425, 455, 585, 630, 1000, 1075, and 1095 nm) could render the model simpler and allow for easy industrial applications.
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Studiul s-a concentrat pe determinarea politicilor guvernamentale esenţiale si a barierelor comerciale care afectează performanţa exporturilor industriei textile în timpul pandemiei de COVID-19. Acest studiu a analizat influenţa politicilor guvernamentale de export asupra performanţei la export a industriei textile. Acest studiu a comparat, de asemenea, factori din trei industrii textile din Asia de Sud, respectiv Pakistan, India si Bangladesh. Studiul a identificat nouă politici guvernamentale de export esenţiale si bariere comerciale bazate pe vizualizarea organizaţiei industriale (Vizualizarea I/O). A fost utilizat un model de regresie de tip panel pentru a analiza semnificaţia fiecărei politici guvernamentale si barierele comerciale care afectează performanţa exporturilor de produse textile. Rezultatele studiului au arătat că ratele de schimb valutar, costul de export, timpul de export, stabilitatea politică a ţării, calitatea infrastructurii din ţară, libertatea din corupţie, costul de afaceri al terorismului si stabilitatea economică în ţară au un efect semnificativ asupra performanţei la export a industriei. În schimb, taxele pentru desfăşurarea afacerilor au un efect nesemnificativ asupra performanţei la export. Testul de Estimare aparent fără legătură (SUEST) a comparat diferenţele de performanţă la export ale industriilor textile din Pakistan, India si Bangladesh datorate politicilor guvernamentale. Rezultatele au arătat că un nivel mai ridicat de timp pentru export, costul de export si costul pentru desfăsurarea afacerilor terorismului duc la performanţa scăzută la export a industriei textile. În acelasi timp, un nivel mai ridicat al cursurilor de schimb valutar, stabilitatea politică a ţării, calitatea infrastructurii, libertatea din corupţie si stabilitatea economică în ţară duc la performanţe ridicate la export ale industriei textile. Mai mult, taxele pentru desfăsurarea afacerilor au un efect nesemnificativ asupra performanţei la export. Acest studiu este printre puţinele care abordează industria textilă în timpul pandemiei de COVID-19. Din cauza circumstanţelor incerte, va fi greu pentru guvern să identifice factori importanţi care ar putea ajuta exportatorii de textile să supravieţuiască si să se dezvolte în timpul pandemiei de COVID-19. Studiul a identificat politici guvernamentale importante si bariere comerciale care afectează exporturile de textile pe baza unui sprijin teoretic solid si a comparat si a elaborat, de asemenea, importanţa fiecărui factor în trei ţări din Asia de Sud. Acest studiu va ajuta factorii de decizie să-si reconsidere factorii legaţi de export pentru a-si spori exporturile de textile si pentru a-si relansa economia după pandemia de COVID-19.Alternate :The study focused on determining essential government policies and trade barriers affecting the textile industry's export performance during the COVID-19 pandemic. This study has analysed the effect of government export policies on the export performance of the textile industry. This study has also compared factors among three South Asian textile industries, including Pakistan, India, and Bangladesh. The study identified nine essential government export policies and trade barriers based on Industrial Organization View (I/O View). A panel regression model was used to analyse the significance of each government policy and trade barrier affecting textile export performance. Results of the study showed that currency exchange rates, the cost to export, time to export, political stability of the country, quality of infrastructure in the country, freedom from corruption, business cost of terrorism and economic stability in the country have a significant effect on export performance of the industry. In contrast, taxes on doing business have an insignificant effect on export performance. The Seemingly Unrelated Estimation (SUEST) test compared the differences in export performance of Pakistani, Indian and Bangladeshi textile industries due to governmen policies. The results showed that a higher level of time to export, cost to export and business cost of terrorism lead to the low export performance of the textile industry. At the same time, a higher level of currency exchange rates, political stability of the country, quality of infrastructure, freedom from corruption and economic stability in-country lead to the high export performance of the textile industry. Further, taxes on doing business have an insignificant effect on export performance. This study is among the few contributing to the textile industry during the COVID-19 pandemic. Due to uncertain circumstances, it becomes hard for the government to identify important factors which could help textile exporters to survive and grow during the COVID-19 pandemic. The study has identified important government policies and trade barriers affecting textile exports based on strong theoretical support and has also compared and elaborated on the importance of each factor across three South Asian countries. This study will help policymakers reconsider exportrelated factors to enhance their textile exports and revive their economy after the COVID-19 pandemic.
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India was the second highest COVID-19 affected country in the world with 2.1 million cases by 11th August. This study focused on the spatial transmission of the pandemic among the 640 districts in India over time, and aimed to understand the urban-centric nature of the infection. The connectivity context was emphasized that possibly had inflicted the outbreak. Using the modes of transmission data for the available cases, the diffusion of this disease was explained. Metropolitans contributed three-fourths of total cases from the beginning. The transport networks attributed significantly in transmitting the virus from the urban containment zones. Later, there was a gradual shift of infections from urban to rural areas; however, the numbers kept increasing in the former. The massive reverse migration after lockdown spiked the infected cases further. Districts with airports reported more with influx of international passengers. A profound east-west division in April with higher infections in the southern and western districts existed. By mid-May eastern India saw a steep rise in active cases. Moran's I analysis showed a low autocorrelation initially which increased over time. Hotspot clustering was observed in western Maharashtra, eastern Tamil Nadu, Gujarat and around Kolkata by the second week of August. The diffusion was due to travel, exposure to infected individuals and among the frontline workers. Spatial regression models confirmed that urbanization was positively correlated with higher incidences of infections. Transit mediums, especially rail and aviation were positively associated. These models validated the crucial role of spatial proximity in diffusion of the pandemic.
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The Fintech sector has grown rapidly since the 2008 global financial crisis. The growth of the industry has thereafter been shaped by the COVID-19 pandemic, a crisis with substantial implications for economic stability. The risk profile of fintech firms was examined using the CRISP-DM framework, which facilitated the classification and clustering of algorithms and regression models. This paper provides insights into assessing financial risk by combining econometric modeling and machine learning techniques.
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The COVID-19 death predictions are helpful for the formulation of public policies, allowing the use of more effective social isolation strategies with less economic and social impact. This article evaluates a wide range of forecasting methods to identify the best models for predicting cumulative and daily deaths caused by COVID-19 in Brazil, considering a forecast for a seven-day horizon. With the seven-day horizon, the predictions have more accuracy. The dataset is from Oxford Covid-19 Government Response Tracker. The jackknife resampling technique was implemented, thus providing an accurate estimate for evaluating the predictive capacity of the models. Each model was fitted with 266 jackknife samples considering 30-day training bases. The comparison between predictions was made using the average results, considering R-2, MAPE, RMSE, and MAE. Models from different classes were adopted: 1 ETS, 4 ARIMA, 18 regression models, and 7 machine learning algorithms. The cumulative death models produce better results than daily deaths, as the cumulative death models are less influenced by time series components: cycle and seasonality. The best results for predicting daily deaths were attained by the Ridge regression method. The best results for predicting cumulative deaths were obtained by the Cubist regression method.
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This study examines the response of the Consumer Price Index (CPI) in local currency to the COVID-19 pandemic using monthly data (March 2020–February 2022), comparatively for six European countries. We have introduced a model of multivariate adaptive regression that considers the quasi-periodic effects of pandemic waves in combination with the global effect of the economic shock to model the variation in the price of crude oil at international levels and to compare the induced effect of the pandemic restriction as well and the oil price variation on each country's CPI. The model was tested for the case of six emergent countries and developed European countries. The findings show that: (i) pandemic restrictions are driving a sharp rise in the CPI, and consequently inflation, in most European countries except Greece and Spain, and (ii) the emergent economies are more affected by the oil price and pandemic restriction than the developed ones.
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Despite the anecdotal evidence that construction women workforces have faced difficulties in accessing adequate and properly fitting personal protective equipment (PPE), there have been very few studies addressing their experiences and satisfaction with PPE. This study aimed to provide an overview of women workforces' satisfaction with PPE in the Australian construction industry. The specific research objectives were to: (i) examine their satisfaction regarding the functional, expressive and aesthetic (FEA) need attributes of PPE and (ii) investigate factors affecting their overall satisfaction with PPE. Data were collected using an online questionnaire survey. The results indicated a rather low satisfaction level among the respondents for all the thirteen FEA need attributes of their PPE. A regression model showed that their overall satisfaction with PPE was significantly affected by their experiences of PPE use (i.e., the need for alterations or adjustments to PPE, adequacy of training for PPE use, the perceived impact of ill-fitting PPE on work productivity) and satisfaction with FEA need attributes but not their demographical factors. The research findings call for action among construction training organizations, PPE designers and manufacturers and construction employers to recognise and address the low satisfaction level for PPE use among women workforces in the industry.
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Greenspaces are argued to be one of the important features in the urban environment that impact the health of the population. Previous research suggested either positive, negative, or no associations between greenspaces and health-related outcomes. This paper takes a step backward to, first, explore different quantitative spatial measures of evaluating greenspace exposure, before attempting to investigate the relationship between those measures and health-related outcomes. The study uses self-reported health data from an online cross-sectional survey conducted for residents in the West of England. This yielded data of greenspace use, physical activity, wellbeing (ICECAP-A score), and connectedness to nature for 617 participants, divided into two sets: health outcomes for the period before versus during the 2020 lockdown. The study uses the participants' postcodes (provided in the survey) to calculate eleven spatial measures of greenspace exposure using the software ArcGIS Pro 2.9.5. A total of 88 multivariate regression models were run while controlling for eleven confounders of the participants' characteristics. Results inferred 57 significant associations such that six spatial measures of greenspace exposure (NDVI R200m, NDVI R300m, NDVI R500m, Network Distance to nearest greenspace access, Euclidean Distance to nearest greenspace access, and Euclidean Distance to nearest 0.5 ha doorstep greenspace access) have significant association to at least one of the four health-related outcomes, suggesting a positive impact on population health when living in greener areas or being closer to greenspaces. Moreover, there are further significant associations between the frequency of use of greenspaces and increasing physical activity or feeling more connected to nature. Still, the residents' patterns of using greenspaces significantly changed during versus before lockdown and has impacted the relationships between health outcomes and the greenspace exposure measures.
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The COVID-19 pandemic in 2020 prompted higher education institutions in the United Arab Emirates (UAE) to switch to online learning for the safety of their citizens. The main purpose of this study is to determine the relationship between four indicators of digital learning experience and the intensity of student socio-pedagogical communication after the transition to distance learning. The data were collected from Ajman University, a private university in the UAE, during the spring of 2020. The sample consisted of 381 students who were surveyed using an online survey tool or email. First, this study found that the majority of students had access to digital tools and the Internet;however, a small number struggled with weak and unreliable Internet connection. Most students had a moderate to high ability to use digital technology, but some encountered difficulties and required assistance. Most students utilised digital communication tools for over five hours daily. The study also found a general lack of digital competency among students and difficulties in using digital tools for remote learning, highlighting the importance of investing in the development of digital skills. The study also found an intensification of social relationships and an increase in communication frequency between students and instructors;however, inadequate instructor–student communication remained a challenge. Finally, the multiple linear regression model showed that indicators such as the communication dimension of the lessons and the participatory nature of the courses positively impacted the intensity of student communication after the transition to distance learning.
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In recent years, environmental degradation and the COVID-19 pandemic have seriously affected economic development and social stability. Addressing the impact of major public health events on residents' willingness to pay for environmental protection (WTPEP) and analyzing the drivers are necessary for improving human well-being and environmental sustainability. We designed a questionnaire to analyze the change in residents' WTPEP before and during COVID-19 and an established ordinary least squares (OLS), spatial lag model (SLM), spatial error model (SEM), geographically weighted regression (GWR), and multiscale GWR to explore driver factors and scale effects of WTPEP based on the theory of environment Kuznets curve (EKC). The results show that (1) WTPEP is 0–20,000 yuan before COVID-19 and 0–50,000 yuan during COVID-19. Residents' WTPEP improved during COVID-19, which indicates that residents' demand for an ecological environment is increasing;(2) The shapes and inflection points of the relationships between income and WTPEP are spatially heterogeneous before and during COVID-19, but the northern WTPEP is larger than southern, which indicates that there is a spatial imbalance in WTPEP;(3) Environmental degradation, health, environmental quality, and education are WTPEP's significant macro-drivers, whereas income, age, and gender are significant micro-drivers. Those factors can help policymakers better understand which factors are more suitable for macro or micro environmental policy-making and what targeted measures could be taken to solve the contradiction between the growing ecological environment demand of residents and the spatial imbalance of WTPEP in the future.
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The article aims to show that reliable IT support was crucial for the survival and sustainability of organisations during the COVID-19 pandemic. The article considers the negative effect of the crisis caused by the COVID-19 pandemic on the organisational sustainability of an organisation (i.e., organisational performance through employee job performance). It explores the role of IT reliability in mitigating such a negative effect. To verify the hypotheses, the empirical studies were performed during the COVID-19 crisis with 1160 organisations operating in Poland, Italy and the USA. The data were analysed using multiple linear regression models with mediators and moderators. The results confirmed that due to the ability to limit the severity of a crisis-induced negative effect on employee job performance (influencing organisational performance), IT reliability could be considered a mitigator for the negative effect of the COVID-19 crisis on the sustainability of organisations. The results indicate that IT reliability should be fostered among organisations operating during the COVID-19 pandemic to maintain sustainability. © 2023 Katarzyna Tworek, published by Sciendo.