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This paper aims to investigate the effect of firms’ sustainability practices on firm performance and valuation during the COVID-19 pandemic.

Using a sample of Australian listed firms from 2011 to 2021, the authors perform textual analysis on sustainability practices from annual reports and sustainability report disclosures and include this variable in various regression models that assess firm valuation. The authors also use propensity score matching and Heckman two-stage regression methodology to address endogeneity concerns.

The authors find that firms disclosing sustainability practices exhibit higher market valuations relative to other firms. Specifically, loss-making firms exhibit higher market valuation during the COVID-19 crisis relative to prior period. The authors also observe a negative association between sustainability practices and firm performance proxied by return on assets. The findings suggest that engagement in sustainable practices helps loss-making firms remain resilient during the pandemic. In addition, the authors find that the positive relation between sustainability practices and firm value is stronger among firms with a higher level of annual report readability.

Considering the conflicting evidence in the literature on the economic benefits of sustainability practices, this study takes advantage of the heterogeneity in corporate practices and provides empirical evidence that a firm’s sustainability practices can build economic resilience during the COVID-19 pandemic crisis. The authors believe the findings of the study is timely in informing the regulators and standard-setters on changes in reporting required to increase sustainability in the business practices.

The purpose of this paper is to examine whether the quality of management can mitigate the positive association between corporate tax avoidance and firm-specific stock price crash risk (SPCR).

The study is based on data from the Center for Research in Security Prices (CRSP), Compustat and ExecuComp and focuses on US-listed firms from 1980 to 2016. The authors employ ordinary least squares (OLS) regression as the baseline methodology and use five measures of tax avoidance and three measures of SPCR. Propensity score matching (PSM) and two-stage least squares methodologies are employed to address endogeneity concerns.

The authors find that more able managers weaken the positive relationship between tax avoidance and SPCR. The results suggest that the benefits of efficient tax management are more likely in firms with a more able management team as the likelihood of SPCR due to tax avoidance practices is reduced in such firms.

This study has important practical implications for investors who are concerned about firms that engage in tax planning activities that can reduce corporate taxes, but at the same time increase the SPCR. Considering the compelling arguments and the “dark” side of more able managers who may engage in opportunistic behaviour, the study provides useful evidence in support of more able managers.

This paper contributes to the SPCR literature by examining the effect of managerial ability on the likelihood of tax avoidance causing SPCR. Able managers are likely to lower the risk faced by investors and are less likely to extract rent and manipulate information. Therefore, the findings of this study have implications for investors by informing them of the negative value implications of tax avoidance and how they can be mitigated by hiring more able managers.

The study has endeavored to assay the nexus between the converged version of the International Financial Reporting Standards (IFRS) on the performance of the Indian-listed manufacturing firms.

The study has randomly accessed the data of the Bombay Stock Exchange (BSE) listed Indian manufacturing firms using the Prowess IQ database. It has covered 2014–2016 as pre-IFRS and 2017–2020 as the post-IFRS convergence period. Moreover, the study has followed a longitudinal research design with cross-sectional time-series data and has used the difference-in-difference (DiD) technique to assess the effect of the IFRS convergence on firm performance (FP).

The results have indicated that the adoption of the Indian Accounting Standards (Ind AS) has unlikely reported better FP. It has concurred policy implications as full adoption rather than convergence could reap the benefits of the IFRS.

It has contributed to the existing body of knowledge by assaying the effect of the IFRS convergence on FP in developing economies like India using the DiD methodology. The study is an original piece of research and is free from plagiarism.

This study aims to identify several perspectives that affect the adoption of blockchain technology in India (BCTA) and evaluate their impact. To study the sector’s influence on adoption and the impact of BCTA on the performance of the Indian healthcare supply chain (HSCP) using BCTA as a mediating variable.

In this study, we first developed a conceptual model based on Organizational Information Processing Theory and Technology-Organization-Environment, then formulated hypotheses. Based on this, a questionnaire was developed, and data were gathered from experts in the Indian healthcare industry who were familiar with blockchain technology. AMOS 19 was used to analyze data using structural equation modelling.

All the factors have a significant positive influence on BCTA. Healthcare supply chain factors influenced the adoption most dominantly, followed by technological, environmental, organizational and record-keeping unit factors. Both the public and private sectors of HSCP benefited significantly from BCTA.

This research work is fruitful for healthcare practitioners, top management, academicians and policymakers in assessing BCTA’s impact on the HSCP.

We have attempted to evaluate the possible BCTA impact on HSCP. BCTA as a mediating variable and considering different perspectives for a holistic view of adoption in the Indian context add to this work’s originality.

Social Media is one of the largest platforms to voluntarily communicate thoughts. With increase in multimedia data on social networking websites, information about human behaviour is increasing. This user-generated data are present on the internet in different modalities including text, images, audio, video, gesture, etc. The purpose of this paper is to consider multiple variables for event detection and analysis including weather data, temporal data, geo-location data, traffic data, weekday’s data, etc.

In this paper, evolution of different approaches have been studied and explored for multivariate event analysis of uncertain social media data.

Based on burst of outbreak information from social media including natural disasters, contagious disease spread, etc. can be controlled. This can be path breaking input for instant emergency management resources. This has received much attention from academic researchers and practitioners to study the latent patterns for event detection from social media signals.

This paper provides useful insights into existing methodologies and recommendations for future attempts in this area of research. An overview of architecture of event analysis and statistical approaches are used to determine the events in social media which need attention.

This study aims to explore and unfold the problems in designing and delivering employer-initiated financial education programs (FEPs) from the perspective of working women who attend such programs.

The researchers conducted in-depth interviews and utilized an interpretive qualitative approach to explore the expectations and experiences of women employees regarding such programs.

The results of this study demonstrate that employer-led FEPs may not benefit women employees due to specific misaligned actions of both employers and employees at every stage, which make the programs ineffective.

While this study encompasses women from varied age groups and marital statuses, the researchers acknowledge that the sample size is limited and represents a specific socioeconomic group.

The findings of this study have policy and practical implications for addressing perceived issues in FEPs initiated by employers for women employees.

The novel contributions of this study include suggesting a process model for building FEPs, highlighting the existing problems at each step in designing and delivering an FEP and expanding the application of Self-Determination Theory in FEPs.

The latest 2019 coronavirus (COVID-2019), which first appeared in December 2019 in Wuhan's city in China, rapidly spread around the world and became a pandemic. It has had a devastating impact on daily lives, the public's health and the global economy. The positive cases must be identified as soon as possible to avoid further dissemination of this disease and swift care of patients affected. The need for supportive diagnostic instruments increased, as no specific automated toolkits are available. The latest results from radiology imaging techniques indicate that these photos provide valuable details on the virus COVID-19. User advanced artificial intelligence (AI) technologies and radiological imagery can help diagnose this condition accurately and help resolve the lack of specialist doctors in isolated areas. In this research, a new paradigm for automatic detection of COVID-19 with bare chest X-ray images is displayed. Images are presented. The proposed model DarkCovidNet is designed to provide correct binary classification diagnostics (COVID vs no detection) and multi-class (COVID vs no results vs pneumonia) classification. The implemented model computed the average precision for the binary and multi-class classification of 98.46% and 91.352%, respectively, and an average accuracy of 98.97% and 87.868%. The DarkNet model was used in this research as a classifier for a real-time object detection method only once. A total of 17 convolutionary layers and different filters on each layer have been implemented. This platform can be used by the radiologists to verify their initial application screening and can also be used for screening patients through the cloud.

This study also uses the CNN-based model named Darknet-19 model, and this model will act as a platform for the real-time object detection system. The architecture of this system is designed in such a way that they can be able to detect real-time objects. This study has developed the DarkCovidNet model based on Darknet architecture with few layers and filters. So before discussing the DarkCovidNet model, look at the concept of Darknet architecture with their functionality. Typically, the DarkNet architecture consists of 5 pool layers though the max pool and 19 convolution layers. Assume as a convolution layer, and as a pooling layer.

The work discussed in this paper is used to diagnose the various radiology images and to develop a model that can accurately predict or classify the disease. The data set used in this work is the images bases on COVID-19 and non-COVID-19 taken from the various sources. The deep learning model named DarkCovidNet is applied to the data set, and these have shown signification performance in the case of binary classification and multi-class classification. During the multi-class classification, the model has shown an average accuracy 98.97% for the detection of COVID-19, whereas in a multi-class classification model has achieved an average accuracy of 87.868% during the classification of COVID-19, no detection and Pneumonia.

One of the significant limitations of this work is that a limited number of chest X-ray images were used. It is observed that patients related to COVID-19 are increasing rapidly. In the future, the model on the larger data set which can be generated from the local hospitals will be implemented, and how the model is performing on the same will be checked.

Deep learning technology has made significant changes in the field of AI by generating good results, especially in pattern recognition. A conventional CNN structure includes a convolution layer that extracts characteristics from the input using the filters it applies, a pooling layer that reduces calculation efficiency and the neural network's completely connected layer. A CNN model is created by integrating one or more of these layers, and its internal parameters are modified to accomplish a specific mission, such as classification or object recognition. A typical CNN structure has a convolution layer that extracts features from the input with the filters it applies, a pooling layer to reduce the size for computational performance and a fully connected layer, which is a neural network. A CNN model is created by combining one or more such layers, and its internal parameters are adjusted to accomplish a particular task, such as classification or object recognition.