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The authors seek to stimulate and strengthen learning for both institutional and corporate leadership to transform society toward sustainability and resilience. The authors use sustainability in the broader socioecological sense, rather than meaning merely financial survival. Based upon experiences by various parties in dealing with coronavirus disease 2019 (COVID-19) or (C-19) during 2020 and into 2021, we are all driven to ask, “Which lessons shall we learn?”

Based upon a brief review of environmental and management literature, the authors compare experiences with C-19 and those of socio-ecological sustainability to-date and distill both sources for optimism as well as pessimism in the face of technical and socio-political challenges.

Historical experiences are not particularly encouraging, but there are many opportunities for great improvements if institutional and corporate leaders choose to learn from both C-19 experiences and earlier efforts toward sustainability.

Procrastination by major industrialized economies in not taking major positive actions to control and reduce carbon pollution and other environmental damage is leading to human crises–hunger and thirst followed by migration, conflicts and healthcare system collapses. Organizational executives need to develop flexibility and embrace precautionary principles regarding many stakeholders if humanity is going to have a good chance of flourishing in the future.

The authors adapt the “wedding cake” model of Sustainability Development Goals (SDGs) and their relationships to the concept of “dynamic materiality” in both an organizational as well as a macro perspective. In addition, the authors introduce the word sustilience to describe an organization's combined ability to achieve “sustainability” through relatively stable conditions as well as the “resilience” to rebound after major external shocks.

This paper aims to assess COVID-19 (C-19) pandemic influence in the 37 factors identified from extant literature as factors influencing the viability of local construction firms (LCFs).

A sample size of 65 staff of 31 LCFs that were awarded construction projects contracts in institutions in Nigeria was purposefully selected and accessed based on relevant predetermined criteria. Respondents’ views on factors determining the viability of LCFs were obtained. Factors known to be influenced by C-19 are 25 of the 37 factors rated on a five-point Likert scale of importance by the respondents. Mean scores were used to rank the factors and principal component analysis was used to obtain key component factors (CFs) influenced by the C-19 pandemic.

Six of the first ten “extremely important” and “very important” factors are known to be influenced by C-19 pandemic. A total of 8 CFs having 20 variables with factor loadings of more than 0.5 each were known to be influenced by C-19. The C-19 pandemic influenced LCFs’ cash flow and management of construction labour, plant and equipment amidst variables that had above 0.8 factor loading.

A limitation of this study is the inability to conduct close contact interview during this period to obtain personal views on the influence of C-19 on LCFs. However, this does not reduce the quality of findings of this study, as there are valid literature basis hinging this study findings.

The paper recommends that all stakeholders pay prompt attention to the factors adversely affected by the C-19 pandemic to improve or at the least sustain the viability of LCFs.

This paper fulfils a present pertinent need of assessing the influence of the C-19 pandemic on various factors influencing the viability of construction firms.

The author's purpose is to identify and analyze the progress of proposals and dissertations after mentor–mentee relationships rapidly transitioned to intensive online doctoral mentoring as a result of coronavirus 2019 (COVID-19).

An exploratory pedagogic research design was implemented in 2020 to examine the COVID-19 Dyadic Online Mentoring Intervention, a four-month individualized approach to mentorship. A survey was completed by mentees in an educational leadership cohort that revealed the benefits and drawbacks of technology for learning within online doctoral mentoring contexts. Additional sources of data were published literature, mentor's notes, email exchanges, and scholarly enrichment products.

Data analysis yielded three themes: (1) mentoring strategies were utilized; (2) the pandemic unsettled reality and (3) personal professional development opportunities were evident. Although life challenges were exacerbated by the pandemic, the online doctoral mentoring intervention met dissertation-related needs and supported academic progress in a Doctorate in Education degree program.

Technology-mediated mentoring during crises involves more than modality changes. Faculty mentors should not be solely responsible for mitigating program and dissertation disruption. Academic cultures must support the adoption of pedagogic innovations like high-quality online doctoral mentoring.

Online doctoral mentoring structures utilizing synchronous and asynchronous technologies can help mentees make academic progress in a crisis, not only in “normal” times.

This study aims to empirically investigates the effect of the COVID-19 pandemic on ethical and nonethical stocks in Indonesia. Ethical stocks which are characterized by moral-based companies’ activities and lower debt are expected to have better resilience during the COVID-19 crisis compared to nonethical stock.

This study observes 589 firms of ethical and nonethical stock during sample periods ranging from March 2, 2020 (first case announced) to June 30, 2021. Panel regression, with some control variables, was applied.

Testing firms in Indonesia revealed a significant difference in stock resilience, in which ethical stock has a better resilience compared to nonethical, with Islamic socially responsible investment (SRI) stock having the highest resilience, followed by Islamic stock and then SRI stock. This study documents a significant effect of some financial criteria on the stock resilience, namely, return market (RM), market capitalization (MCAP) and share turnover (TURN). Overall, after splitting the sample into different time horizons, this study consistently reveals that ethical firms have better resilience compared to nonethical stocks.

This study makes several contributions to the literature on Islamic finance, especially concerning Islamic screening with SRI factors. In practical terms, this study supports the argument that focusing on integrating environmental, social and governance criteria in sharia screening will improve the quality of Islamic firms. The “Islamic” label is not only a marketing label but also a quality certification.

This study can be used as a reference for developing Islamic finance more focused on sustainability issues including socioeconomic and human development by improving the quality of screening of Islamic firms. Therefore, this study suggests that the establishment of Islamic SRI index is very crucial and significant to promote ethical-based investment.

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.

This article explores how internal communication impacts happiness at work and intrapreneurship through the dimensions of communication climate and communication in meetings.

Literature analysis and quantitative empirical data collection are used to achieve this study's purpose. The study employed a non-experimental, cross-sectional, explanatory design. A questionnaire of closed-ended questions was used, adapted from validated scales for measuring constructs, and apply to 156 employees of industrial, service and commercial companies in the Northeast of Mexico. The analysis techniques used included exploratory factor analysis and structural equation modelling.

In the descriptive analysis of the data, the authors find that 57.7% of the sample is satisfied with the communication climate, 28.85% moderately satisfied, and 13.47% dissatisfied. Regarding the levels of happiness at work, high (71.15%), medium (21.15%) and low (7.7%) levels were found. In intrapreneurship, high (67.31%), medium (26.92%) and low (5.77%) levels were found. The exploratory factorial analysis showed that the instrument was adequate for measuring the variables. Good correlations were also found between the items that make up each variable. Finally, the relationship between internal communication, measured by the dimensions of communicative climate and communication in meetings, and happiness at work was verified using the structural equation technique. The latter has a positive impact on intrapreneurship.

This article has some theoretical and methodological limitations like any other academic work. They would be interesting to address in future research. In this way, it is possible to empirical examine the variables of intrapreneurship, internal communication and happiness (Ravina-Ripoll et al., 2021c). The first is the study's cross-sectional design and data collection by a non-probabilistic sample, carried out in a single source. Both aspects mean that our study is not free of corresponding biases; this may result in the findings of the present work not being statistically correct. The second derives from the absence in the literature of structural equation modelling studies that analyse the constructs that make up the object of this academic work in a multidimensional way. However, although an influence I show between the variables, it is recommended to take the data with discretion. There is still a need for more empirical evidence to support these relationships before generalised results can be presumed. Despite the remarkable progress made in recent years in the literature on the three dimensions of this article, few scientific studies examine inferentially how internal communication and intra-entrepreneurship influence employees' happiness at work in today's digital society. The authors of this academic work consider it attractive for future research to address the analysis of internal communication strategic management models. It is a robust driver of intra-entrepreneurship and employee happiness in organisations (Galván-Vela et al., 2022a). In conclusion, from this heuristic perspective, companies can improve, on the one hand, their competitive position in the market. Their managers must cultivate an organisational culture that emphasises internal communication as a catalyst for innovation, employee loyalty, and productive efficiency. On the other hand, companies will be able to invigorate their corporate image to face the significant challenges in the globalised economy, thus to become sustainable, humane, ecological intra-entrepreneurial corporations (Galván-Vela et al., 2021a). It may lead to a more social, inclusive, prosperous and egalitarian ecosystem. In this way, it makes the culture of organisations around the pillars of happiness management, social marketing and the Sustainable Development Goals shine (Galván-Coronil et al., 2021).

This section does not attempt to argue that internal communication and intrapreneurship constitute two intangible resources that improve organisations' productivity and collective happiness (Lee and Kim, 2022). However, it is necessary to clarify that the results achieved in this academic study show two fundamental aspects. The first is to invite managers of companies in the post-Covid-19 era to cultivate a culture based on happiness management. It makes internal communication a fast vehicle that exponentially boosts intrapreneurship, among other things (Castillo-Abdul et al., 2021). To this end, their strategic management models must carry out a diametrical shift in their innovation and internal communication actions. On the one hand, it allows for building loyalty among their creative talent. It does this by creating an organisational climate that encourages interpersonal relationships, the spirit of teamwork, collaborative participation, and disruptive thinking (Thelen and Formanchuk, 2022). On the other hand, promoting an ethical, assertive and empathetic leadership style proactively stimulates the commitment, trust and passion for the work of all members of the company (Men and Yue, 2019). The second is to emphasise implementing a constructive, friendly and positive intra-organisational language. In this way, it is dynamising the collective happiness of its human capital through the figure of the Chief Happiness (Jiménez-Marín et al., 2021b).

Intrapreneurship, internal communication and happiness at work are topics of great interest in academic agendas in recent years. It is basically because these three dimensions, individually or jointly, have positive effects on the productivity of organisations. However, no research flow evidences our theoretical model proposed in this article. Therefore, there is a need for future studies that advance the literature in the area of business. In this way, we will have more data on how these constructs affect the life of organisations in the post-Covid 19 eras.

The purpose of the paper is to investigate co-movement of major implied volatility indices and economic policy uncertainty (EPU) indices with both the health-based fear index and market-based fear index of COVID-19 for the USA and the UK to help investors and portfolio managers in their informed investment decisions during times of infectious disease spread.

This study uses wavelet coherence approach because it allows to observe lead–lag nonlinear relationship between two time-series variables and captures the heterogeneous perceptions of investors across time and frequency. The daily data used in this study about the USA and the UK covers major implied volatility indices, EPU, health-based fear index and market-based fear index of COVID-19 for both the first and second waves of COVID-19 pandemic over the period from March 3, 2020 to February 12, 2021.

The results document a strong positive co-movement between implied volatility indices and two proxies of the COVID-19 fear. However, in all the cases, the infectious disease equity market volatility index (IDEMVI), the COVID-19 proxy, is more representative of the stock market and exhibits a stronger positive co-movement with volatility indices than the COVID-19 fear index (C19FI). This study also finds that the UK’s implied volatility index weakly co-moves with the C19FI compared to the USA. The results show that EPU indices of both the USA and the UK exhibit a weak or no correlation with the C19FI. However, this study finds a significant and positive co-movement of EPU indices with IDEMVI over the short horizon and most of the sampling period with the leading effect of IDEMVI. This study’s robustness analysis using partial wavelet coherence provides further strengths to the findings.

The investment decisions and risk management of investors and portfolio managers in financial markets are affected by the new information on volatility and EPU. The findings provide insights to equity investors and portfolio managers to improve their risk management practices by incorporating how health-related risks such as COVID-19 pandemic can contribute to the market volatility and economic risks. The results are beneficial for long-term equity investors, as their investments are affected by contributing factors to the volatility in US and UK’s stock markets.

This study adds following promising values to the existing literature. First, the results complement the existing literature (Rubbaniy et al., 2021c) in documenting that type of COVID-19 proxy matters in explaining the volatility (EPU) relationships in financial markets, where market perceived fear of COVID-19 is appeared to be more pronounced than health-based fear of COVID-19. Second, the use of wavelet coherence approach allows us to observe lead–lag relationship between the selected variables, which captures the heterogeneous perceptions of investors across time and frequency and have important insights for the investors and portfolio managers. Finally, this study uses the improved data of COVID-19, stock market volatility and EPU compared to the existing studies (Sharif et al., 2020), which are too early to capture the effects of exponential spread of COVID-19 in the USA and the UK after March 2020.

In a pandemic framework (COVID-19), this chapter explores the impact of the global economy and socio-cultures concerning three axes: recreational, tourism, and hospitality. Although we slowly see an economic revival, it is well known that this sector of study is very susceptible to being affected by the context of nations. Following restrictions and measures taken by governments around the world to reduce the number of cases of coronavirus infections, many nations closed their borders, affecting international travel and by 2020 tourism had been reduced to the near cessation of operations due to the imminent fear of this poorly studied disease, and the service sector was negatively affected. It should be added that, according to the World Tourism Organization's projections, a decrease of between 20 and 30% is forecast for 2020 compared to the previous year.

We aim to analyze the capital structure heterogeneity for manufacturing and service sector firms. Additionally, we analyze the impact of the COVID-19 pandemic on the leverage adjustments of corporate firms.

This study applies the two-step system generalized method of moments (system-GMM) and panel data of 1,115 manufacturing and 482 service sector firms listed with the Bombay Stock Exchange (S&P BSE) from 2010 to 2023. We developed and analyzed three models. Model 1 analyzes the leverage determinants and speed of adjustment (SOA) for the manufacturing and service sectors. Model 2 evaluates the leverage SOA for various sub-sectors, and Model 3 analyzes the impact of the COVID-19 pandemic on the leverage SOA.

This study suggests the three following. First, the direction of leverage determinants suggests that manufacturing firms are highly tangible. In contrast, service sector firms are high-growth firms and recorded a higher SOA (12.01%) than manufacturing (9.09%). Second, analyzing the leverage heterogeneity, we found that SOA varies across the sub-sectors. For manufacturing, food and beverage sub-sector recorded the highest SOA (12.58%), while consumer durables reported the lowest (6.38%). Communication recorded the highest (24.15%) for services, while industrial services recorded the lowest (11.18%). Third, firms across sectors and sub-sectors increased their SOA during COVID-19 pandemic.

This in-depth analysis of leverage heterogeneity for different sectors and subsectors will assist policymakers, corporate managers and other stakeholders in making agile financial decisions.

The analysis of leverage heterogeneity for the manufacturing and service sector from the emerging Indian economy marks a novel contribution to existing literature.

The purpose of this study/paper To focus on finding COVID-19 with the help of DarkCovidNet architecture on patient images.

We used machine learning techniques with convolutional neural network.

Detecting COVID-19 symptoms from patient CT scan images.

This paper contains a new architecture for detecting COVID-19 symptoms from patient computed tomography scan images.