The Moderating Effects of Corporate Social Responsibility on Corporate Financial Performance: Evidence from OECD Countries

This study aims to investigate the nature and intensity of the changes in corporate financial performance due to the corporate social responsibility (CSR) disclosures as a result of certain relationships between corporate governance and company performance in the non-financial sector. This study selected 625 non-financial companies across six organizations for economic cooperations (OECD) countries' stock markets for the period of 10 years (2012–2021). For this qualitative study, corporate governance, financial performance, and corporate social responsibility score data were collected from the DataStream, a reliable database for examining the research on OECD countries' listed companies. For the data analysis we applied various statistical tools such as regression analysis and moderation analysis. The findings of the study show that all attributes of the corporate governance mechanism, except for audit board attendance, have significant positive impacts on financial performance indicators for all the selected OECD economies except the country France. France's code of corporate governance has a significant negative impact on return on asset (ROA) and return on equity (ROE) due to differences in cultural and operational norms of the country. The audit board attendance has no significant impact on ROA. Moreover, all the attributes except board size (BSIZ) have significant positive impacts on the earnings per share (EPS) in Spain, The United Kingdom (UK) and Belgium. The values obtained from the moderation effect show that Corporate social responsibility is the key factor in motivating corporate governance practices which eventually improves corporate financial performance. However, this study advocated the implications, Investors and stakeholders should consider both corporate governance and CSR disclosures when making investment decisions. Companies that prioritize both governance and CSR tend to have better financial performance and are more likely to mitigate risks. Moreover, the policy makers can improve the code of corporate governance in order to attain sustainable development in the stock market.

Multivariate count time series segmentation with "sums and shares” and Poisson lognormal mixture models: a comparative study using pedestrian flows within a multimodal transport hub

This paper deals with a clustering approach based on mixture models to analyze multidimensional mobility count time-series data within a multimodal transport hub. These time series are very likely to evolve depending on various periods characterized by strikes, maintenance works, or health measures against the Covid19 pandemic. In addition, exogenous one-off factors, such as concerts and transport disruptions, can also impact mobility. Our approach flexibly detects time segments within which the very noisy count data is synthesized into regular spatio-temporal mobility profiles. At the upper level of the modeling, evolving mixing weights are designed to detect segments properly. At the lower level, segment-specific count regression models take into account correlations between series and overdispersion as well as the impact of exogenous factors. For this purpose, we set up and compare two promising strategies that can address this issue, namely the "sums and shares” and "Poisson log-normal” models. The proposed methodologies are applied to actual data collected within a multimodal transport hub in the Paris region. Ticketing logs and pedestrian counts provided by stereo cameras are considered here. Experiments are carried out to show the ability of the statistical models to highlight mobility patterns within the transport hub. One model is chosen based on its ability to detect the most continuous segments possible while fitting the count time series well. An in-depth analysis of the time segmentation, mobility patterns, and impact of exogenous factors obtained with the chosen model is finally performed. © 2023, Springer-Verlag GmbH Germany, part of Springer Nature.

A Multi-market Comparison of the Intraday Lead-Lag Relations Among Stock Index-Based Spot, Futures and Options.

Using 1-min data, we explore the dynamic variation of the intraday lead-lag relations between stock indices and their derivatives through a comprehensive study with broader coverage of research objectives and methodologies. This paper provides explicit evidence that the futures and options exhibit price leadership over the spot market, and the options is ahead of the futures on most trading days in all three markets. This paper also reports a new finding that the relation between the derivative and its underlying index reverses when the index return has a significantly larger mean value, and the reversal phenomenon is also observed in the relations between the futures and the options, which enriches the empirical results of intraday lead-lag relations. Moreover, these conclusions still hold under the impact of extreme events, e.g., the outbreak of the Covid-19. Finally, we construct a pair trading strategy based on the intraday lead-lag relationships, which can get better performance than the corresponding spot index. Our findings can potentially help regulators understand the price discovery process between the index and its derivatives, and also be of great value for timely adjustment of investors intraday trading strategies.

Weathering the crash: Do customer-company relationships pay off during economic crises?

Do stronger relationships with customers (customer-company relationships [CCR]) help firms better weather economic crises? To answer this question, we examine firm performance during the stock market crashes associated with the two most severe economic crises of the last 15 years-the protracted Great Recession crisis (2008-2009) and the shorter but extreme COVID-19 pandemic crisis (2020). Juxtaposing the predominant expected utility theory perspective with observed deviations in investor behavior during crises, we find that both pre-crash firm-level customer satisfaction and customer loyalty are positively associated with abnormal stock returns and lower idiosyncratic risk during a market crash, while pre-crash firm-level customer complaint rate negatively affects abnormal stock returns and increases idiosyncratic risk. On average, we find that one standard deviation higher CCR is associated with between $0.9 billion and $2.4 billion in market capitalization on an annualized basis. Importantly, we find that these effects are weaker for firms with higher market share during the COVID-19 crash, but not during the Great Recession crash. These results are found to be robust to a variety of alternate model specifications, time periods, sub-samples, accounting for firm strategies during the crises, and endogeneity corrections. When compared to relevant non-crash periods, we also find that such effects are equally strong during the Great Recession crash and even stronger during the COVID-19 pandemic crash. Contributing to both the marketing-finance interface literature and the nascent literature on marketing during economic crises, implications from these findings are provided for researchers, marketing theory, and managers. Supplementary information: The online version contains supplementary material available at 10.1007/s11747-023-00947-1.

The Relationship Between Financial Indicators and Financial Intermediaries' Stock Price Volatility Listed on the BM&FBOVESPA Index in the Crisis Period of 2008 and 2020 (Covid-19): GCG

The study goal was to verify the relationship among financial indicators and intermediaries' volatility stock price listed on the BM&FBovespa Index in the crisis period from 2008 and 2020 (COVID-19). The methods used for analysis were Spearman's correlation, multiple linear regression, and Test T. The analyzed period refers to the year 2008, the second semester of 2019 and the first semester of 2020, which include the periods before and during the crises of 2008 and 2020. The results found show that only the indicator of the assets total turnover rate has a significant relationship with the stock price volatility.Alternate :O estudo tem como objetivo verificar a relação entre os indicadores com a volatilidade das ações das intermediadoras financeiras listadas no Índice BM&FBovespa no período das crises de 2008 e 2020 (COVID-19). Os métodos utilizados para análise foram de correlação de Spearman, regressão linear múltipla e Teste T. O período analisado refere-se ao ano de 2008, segundo semestre de 2019 e primeiro semestre de 2020, onde englobam os períodos pré e durante as crises de 2008 e 2020. Os resultados encontrados apontam que apenas o indicador taxa total de rotatividade dos ativos possui relação significativa com a volatilidade do preço das ações.Alternate :El estudio tiene como objetivo verificar la relación entre los indicadores y la volatilidad de las acciones de los intermediarios financieros listados en el Índice BM&FBovespa en el período de las crisis de 2008 y 2020 (COVID-19). Los métodos utilizados para el análisis fueron la correlación de Spearman, la regresión lineal múltiple y la prueba T. El período analizado se refiere al año 2008, la segunda mitad de 2019 y la primera mitad de 2020, que incluyen los períodos antes y durante las crisis de 2008 y 2020. Los resultados encontrados indican que solo el indicador de tasa de rotación de activos totales tiene una relación significativa con la volatilidad del precio de las acciones.

Loneliness and Mental Health in Response to Early and Late COVID-19 Outbreak: A Cross-Sectional Study of Czech Adults Aged 50 and Over.

The unpredictable spread of the COVID-19 pandemic, national lockdowns, and public health measures implemented in the Czech Republic had a negative effect on older adults' mental health and loneliness. The 2,631 older adults in 2020 and 2,083 older adults in 2021 used in this study formed a nationally representative sample of the Survey of Health, Ageing and Retirement in Europe (SHARE). Almost every third older adult experienced feelings of loneliness in both stages of the COVID-19 outbreak. Loneliness increased in 2021 in those individuals who reported their physical health as poor, who felt nervous, sad or depressed, and who had ever left their home since the outbreak. According to age-related drivers of loneliness, feelings of loneliness were prevalent (40% vs. 45%) among younger retirees in both waves. In both data sets, declared feelings of sadness or depression were the strongest sustainable predictor of loneliness (2020 and 2021 models, OR = 3.69; 95% CI [2.90, 4.69] and OR = 2.55; [1.97, 3.30]). Being a woman and feeling nervous equated with a higher likelihood of feeling lonely compared to counterparts. Policy makers should therefore aim to carefully improve psychosocial and health-related consequences faced by this vulnerable population during the pandemic and beyond.

Associations of psychological wellbeing with COVID-19 hospitalization and mortality in adults aged 50 years or older from 25 European countries and Israel.

Background: Lower psychological wellbeing is associated with poor outcomes in a variety of diseases and healthy populations. However, no study has investigated whether psychological wellbeing is associated with the outcomes of COVID-19. This study aimed to determine whether individuals with lower psychological wellbeing are more at risk for poor outcomes of COVID-19. Methods: Data were from the Survey of Health, Aging, and Retirement in Europe (SHARE) in 2017 and SHARE's two COVID-19 surveys in June-September 2020 and June-August 2021. Psychological wellbeing was measured using the CASP-12 scale in 2017. The associations of the CASP-12 score with COVID-19 hospitalization and mortality were assessed using logistic models adjusted for age, sex, body mass index, smoking, physical activity, household income, education level, and chronic conditions. Sensitivity analyses were performed by imputing missing data or excluding cases whose diagnosis of COVID-19 was solely based on symptoms. A confirmatory analysis was conducted using data from the English Longitudinal Study of Aging (ELSA). Data analysis took place in October 2022. Results: In total, 3,886 individuals of 50 years of age or older with COVID-19 were included from 25 European countries and Israel, with 580 hospitalized (14.9%) and 100 deaths (2.6%). Compared with individuals in tertile 3 (highest) of the CASP-12 score, the adjusted odds ratios (ORs) of COVID-19 hospitalization were 1.81 (95% CI, 1.41-2.31) for those in tertile 1 (lowest) and 1.37 (95% CI, 1.07-1.75) for those in tertile 2. As for COVID-19 mortality, the adjusted ORs were 2.05 (95% CI, 1.12-3.77) for tertile 1 and 1.78 (95% CI, 0.98-3.23) for tertile 2, compared with tertile 3. The results were relatively robust to missing data or the exclusion of cases solely based on symptoms. This inverse association of the CASP-12 score with COVID-19 hospitalization risk was also observed in ELSA. Conclusion: This study shows that lower psychological wellbeing is independently associated with increased risks of COVID-19 hospitalization and mortality in European adults aged 50 years or older. Further study is needed to validate these associations in recent and future waves of the COVID-19 pandemic and other populations.

Sociodemographic Factors and Adjustment of Daily Activities During the COVID-19 Pandemic - Findings from the SHARE Corona Survey.

In the wake of the COVID-19 pandemic in 2020, older people across Europe have adjusted their daily activities as personal risk avoidance and as an amendment to policy recommendations and restrictions. In this study, we use multilevel logistic regressions to examine to what extent sociodemographic factors are associated with activity reduction among the older population (50+) in Europe and whether these associations are moderated by governmental policy responses to COVID-19. By combining data for~35,000 respondents from the SHARE Corona Survey on reported changes in daily activities and stringency of restrictions at the national level, we find that older age, poorer health and being female versus male were (consistently) associated with greater activity reduction across all activities both in countries with weak and in those with strong restrictions. Associations between education, employment and living situation, on the one hand, and activity reduction, on the other, were weaker and less consistent. We conclude that differences between sociodemographic groups are rather similar for countries with weak and those with strong restrictions and hence argue that group-specific policy recommendation are relevant independent of stringency recommendations.

Scale effects in ridesplitting: A case study of the City of Chicago

Ridesplitting – a type of ride-hailing in which riders share vehicles with other riders – has become a common travel mode in some major cities. This type of shared ride option is currently provided by transportation network companies (TNCs) such as Uber, Lyft, and Via and has attracted increasing numbers of users, particularly before the COVID-19 pandemic. Previous findings have suggested ridesplitting can lower travel costs and even lessen congestion by reducing the number of vehicles needed to move people. Recent studies have also posited that ridesplitting should experience positive feedback mechanisms in which the quality of the service would improve with the number of users. Specifically, these systems should benefit from economies of scale and increasing returns to scale. This paper demonstrates evidence of their existence using trip data reported by TNCs to the City of Chicago between January and September 2019. Specifically, it shows that increases in the number of riders requesting or authorizing shared trips during a given time period is associated with shorter trip detours, higher rates of riders being matched together, lower costs relative to non-shared trips, and higher willingness for riders to share trips.

User Segmentation Based on Travel Regularity in E-Scooter Sharing Service

The dockless e-scooter sharing service is rapidly spreading, replacing existing transportation, and improving last-mile accessibility. User segmentation with travel regularity and segment-level behavior analysis, which are already conducted in public transit, also benefits e-scooter sharing service to enhance service quality and increase usage. In this work, we group e-scooter users according to their travel regularity and identify each group's usage characteristics. Through the dockless e-scooter usage data, as operated in six cities in South Korea, travel regularity measured by users' repetitive departure time and destination is discovered and spatiotemporal usage patterns are identified. We divide e-scooter users into three groups by type of travel regularity: irregular user, spatially regular user, and regular user. Regular users more frequently use e-scooters, travel shorter distances, and walk longer distances to find an e-scooter than other groups. It is also revealed that the use in morning peak hours only occurs in the regular user group. By decomposing the temporal patterns of spatially regular and regular users, we discover that spatially regular users are composed of daytime, evening peak, and nighttime users. In contrast, regular users are composed of morning peak, evening peak, and lockdown (restriction in response to COVID-19 pandemic) peak users. This research suggests user segmentation based on travel regularity in e-scooter sharing services, enabling multiple strategies to be drawn to retain users with high regularity and convert users with low regularity to regular users.

Exploring the impact of the COVID 19 pandemic on public bicycle sharing systems: the cases of Madrid (BiciMAD) and Buenos Aires (EcoBici)

Public bike-sharing systems (PBSS) allow the use of shared bicycles for a period of time without owning them. In Buenos Aires and Madrid, PBSS were implemented in 2010 and 2014 respectively, managed by public authorities and oriented to find alternatives to mass transit. The advantages offered by PBSS are multiple, and socially and environmentally friendly. The purpose of this article is to conduct an exploration and analysis of the temporal and spatial pattern of trips made using PBSS for both cities before and during the COVID 19 pandemic, in order to contribute to the state of the art. The temporal and spatial patterns in both cities were similar in pre-pandemic times. Both systems were negatively impacted in 2020 in terms of the number of trips due to lockdowns imposed. Both PBSS were closed during the pandemic, but as they were relaunched, bike usage increased. In both cities there is a correlation between the level of supply and the use of the system, and new micromobility spaces were identified. It is relevant to consider the lack of available datasets as well as information processing capabilities.

Factors affecting household carbohydrate food consumption in Central Java: Before and during the COVID-19 pandemic

The COVID-19 pandemic has resulted in a change in food demand. In Central Java, during the pandemic (2021), the proportion of expenditure on the grain food group was higher than in 2020;meanwhile, the proportion of the ready-to-eat food group decreased. This study aims to analyze the pattern of food consumption of carbohydrate sources, the influencing factors, and the elasticity of consumption in households in Central Java before and during the COVID-19 pandemic. This study uses data from the National Socio-Economic Survey (Susenas) for 12 districts/cities in Central Java consisting of 9,812 in 2019 and 10,636 households samples in 2021. Data analysis used the Linear Approximation Almost Ideal Demand System (LA-AIDS) method. Results of the study show that the COVID-19 pandemic has caused changes in the household consumption pattern of carbohydrate-source food in Central Java. The proportion of expenditure on rice, wheat flour, shelled corn, cassava, and potatoes has increased. On the other hand, wet corn, instant noodles, and white rice decreased. The price of food sources of carbohydrates and the number of household members positively affect the consumption of food sources of carbohydrates. At the same time, income has a negative effect. There are differences in the effect of the location of the residence on the consumption of food sources of carbohydrates before and during the pandemic. The value of own-price elasticity and income elasticity shows that before the pandemic, rice was a staple good whose consumption was inelastic. Before and during the COVID-19 pandemic, wheat flour, wet-skinned corn, shelled corn, potatoes, white rice, and cassava in the pre-pandemic period was Giffen because the price elasticity was positive, and the income elasticity was negative. Meanwhile, rice during the pandemic, instant noodles before and during the pandemic, and cassava were Veblen goods because their price and income elasticity were positive. Cross elasticity shows that before the pandemic, most of the relationships between food sources of carbohydrates were substitutes, while during the pandemic, most of the relationships between food sources of carbohydrates were complementary. © 2023 the author(s), published by De Gruyter.

Study of COVID19 Impact on Moroccan Financial Market Based on ARDL

The aim of this work is to study the impact of the COVID-19 pandemic new cases on the Moroccan financial market using the Autoregressive Distributed Lag (ARDL) approach. The analysis focuses on the relationship between the natural logarithm of the Moroccan All Shares Index (MASI) price and the natural logarithm of new daily cases of COVID-19 in the short term as well as in the long term. A cointegration test is performed on the daily time series for the period from March 3, 2020 to February 11, 2022. A causality test of Toda-Yamamoto is also applied on the variables. The implementation of the forecast with the ARDL method improves the forecast accuracy by 8% to achieve 26.7%. The implementation of the forecast with the ARDL method shows that the addition of the lag of COVID19, the trend and the seasonality makes it possible to achieve a MAPE of 26.7% by improving it by 8% compared to the forecast with the lag of the price only. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Covid-19 as an Incubator Leading to Telemedicine Usage: KM Success Factors in Healthcare

Virtual hospitals offer a platform for healthcare workers to share knowledge, treat patients equally everywhere and, thus, reduce patient mortality rates. Such platforms include different technologies, for example telemedical applications. The use of these technologies and the need to get specific knowledge on the patients' treatment was reinforced in the past years due by Covid-19. Not only the treatment of Covid-19, but also that of other diseases can be improved by increased technology use. By incorporating the KM success model, we will identify KM success factors leading to the use of virtual hospitals. This research observes the KM success model in the context of the low-digitalized field of healthcare. Consequently, we evaluate how the existing KM success model needs to be adjusted according to the peculiarities of healthcare. © 2023 IEEE Computer Society. All rights reserved.

Trust and Willingness to Share Health Related Information: A quantitative study of Danish students' use of mHealth applications

mHealth technology has the potential to transform healthcare and realize the goal of precision medicine through systematic data collection and use. Meanwhile, mHealth applications developed during COVID-19 have had limited effect, as people have been reluctant to adopt them due to a lack of trust and willingness to share data. The aim of this empirical study is to provide insights into young people's use, trust, and willingness to share data through mHealth apps as future users of healthcare services. A survey comprising 484 Danish students was conducted. It focuses on mHealth app use, willingness to share data, and trust. The findings show that the trustworthiness of the technology and data requesting organization is important for establishing trust in mHealth apps. These insights indicate how young people can be motivated to trust mHealth apps, which can be used to develop future apps and exploit the untapped potential of the collected data. © 2023 IEEE Computer Society. All rights reserved.

Financial Reportings and Analysis Practices in Tata Steel and Corus Acquisition: Their Association with Growth Rate and Financial Performance

This research article examined the financial reporting and analysis practices in Tata steel and Corus acquisition: Their association with growth rate and financial performance. This article analyzes the reasons and the effect of the Acquisition of Corus by paying 12.33 billion dollars to buy which is one of the biggest acquisitions by an Indian Company at the cross border. This paper also emphasizes this acquisition led to global growth in Steel Industry. The sample size of this research consists of Tata Steel and Corus Company which underwent acquisitions during 2007–08. This paper also emphasizes the significance of developing skills in analyzing and interpreting financial statements to maintain financial position and progress. To understand the distinguishing characteristics, problems and needs of the organization's growth performance, the published audited report from 2002–2022 was studied and interpreted. To measure the significance of the organization's performance the author adopted comparative, ratio analysis and other statistical tools were adopted. The study shows that there is important growth in income and financial position of companies after the merger, the growth can result in financial stresses and excessive use of debts as such cash flow difficulties. But the coronavirus (Lockdown) the company faced a downfall in their profits. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

A Machine Learning Approach to Predict Customer Churn of a Delivery Platform

The use of delivery platforms has become widespread due to the impact of the Covid-19 and the O2O industry. However, the ELEME delivery platform, a subsidiary of Alibaba Group, which represents China, has recently been losing market share. This means that companies need to constantly look at strategies to attract new customers and maintain existing ones. In general, it costs at least five times more to attract new customers than it does to manage existing customers. This paper attempts to predict customer churn using the ELEME customer dataset to develop strategies to identify and prevent churn in advance. The results of the analysis using machine learning approach found that the most influential feature that can predict churn is the number of clicks made by the user. This paper presents the process and explanation of applying various algorithms for predicting customer churn on a distribution platform. It also proposes strategies for dealing with customer churn. © 2023 IEEE.

Coping With Covid‐19: Older Europeans and the Challenges of Connectedness and Loneliness

Social networks are important for well‐being and healthy aging. However, older adults are more likely to have less social contact with others than their younger counterparts due to significant changes in their lives, such as retirement or age‐related losses, along with declining health and mobility. Consequently, with increasing age, a growing proportion of people experience feelings of loneliness. This becomes even more important during pandemics when social contact should be minimized. Therefore, this article examines the extent and patterns of loneliness before and during the first two years of the Covid‐19 pandemic and how social contact and the type of communication affected levels of loneliness during the pandemic. To investigate loneliness, social contact, and their association during the pandemic, this study uses representative data from 27 countries from SHARE (Survey of Health, Ageing, and Retirement in Europe). The analyses are based on a balanced panel covering three consecutive waves with 28,448 respondents aged 50 years or older. The results indicate that three out of ten Europeans face loneliness in later life. While loneliness has increased for a significant part of the elderly in the wake of the pandemic, there has also been a reverse trend in terms of a decrease in feelings of loneliness for an almost equal proportion of people. Additionally, multivariate analyses highlight that nonpersonal communication cannot substitute face‐to‐face interaction and can potentially increase feelings of loneliness. © 2023 by the author(s);licensee Cogitatio (Lisbon, Portugal). This article is licensed under a Creative Commons Attribution 4.0 International License (CC BY).

Predicting Aramco's IPO Long-Term Performance During COVID Times

This paper aims to assess whether the outbreak of the highly contagious pandemic had an impact on the share prices of recently listed Aramco in light of the Fads hypothesis using the methods of neural network and ARIMA. The IPO of Aramco, the world's largest oil company, was a much-hyped affair. Given the relevant importance of the company, it was expected that Aramco's share prices would not underperform in the long run. But the analysis indicates the opposite. The study uses two time periods using the announcement of the pandemic by the World Health Organization as the threshold date to see the impact of the pandemic on Aramco's share prices. The forecasting results validate the Fads hypothesis implying that Aramco's share prices would have underperformed in the long run, even in the absence of a pandemic outbreak. Finally, the study cautions investors against the hype created by IPOs. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

The share of influence of factors determining the state of public health in the context of the COVID-19 pandemic

Background. The relevance of the study is explained by insufficient knowledge of the determinants of health in the context of the COVID-19 pandemic. Aim. To study the share of influence of factors that determine the state of public health in the context of a pandemic of a new coronavirus infection (COVID-19). Material and methods. A sociological research method was used - in 2021, an anonymous survey of 42 expert healthcare managers, who were asked to determine the share of influence of determinants on the state of public health in the context of the COVID-19 pandemic, was conducted. After the completion of the survey, its results were compared with those obtained without a pandemic in 2017. Since the data distribution corresponded to the Gauss-Newton binomial curve, the significance of differences was assessed using the parametric Student's t-test. Results. It has been established that the structure of the influence of health determinants in the context of the COVID-19 pandemic differs significantly from that without it. During a pandemic, the share of factors in the "Lifestyle” group decreases by 1.75 times, and it is no longer a priority, but occupies the third ranking place. Out-of-group determinants, which were in second position, take the first place. The second ranking place with a growth of 1.60 times is occupied by the "Conditions of life and work” group. The share of influence of each of the considered 37 determinants of health was assessed - multidirectional changes, for example, an increase in the responsibility of the authorities for actions and equity in relation to the health of the population from the "General Factors” group and a decrease in six of the seven determinants of the "Lifestyle” group, were identified. Continuation of the study is possible with regard to the formation of a separate block in the design of the appropriate optimization technology. Conclusion. A significant change in the structure of the influence of the public health determinants during the COVID-19 pandemic in comparison with the data obtained before the onset of this pandemic, has been established. © Eco-Vector, 2023. All rights reserved.