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This study aims to facilitate access to vascular disease screening for low-income individuals living in remote and conflict areas based on the results of a pilot trial in Colombia. Also, to increase the amount of diagnosis training of vascular surgery (VS) in civilians.

The operation method includes five stages: strategy development and adjustment; translation of the strategy into a real-world setting; operation logistics planning; strategy analysis and adoption. The operation plan worked efficiently in this study’s sample. It demonstrated high sensibility, efficiency and safety in a real-world setting.

The authors developed and implemented a flow model operating plan for screening vascular pathologies in low-income patients pro bono without proper access to vascular health care. A total of 140 patients from rural areas in Colombia were recruited to a controlled screening session where they underwent serial noninvasive ultrasound assessments conducted by health professionals of different training stages in VS.

The plan was designed to be implemented in remote, conflict areas with limited access to VS care. Vascular injuries are critically important and common among civilians and military forces in regions with active armed conflicts. As this strategy can be modified and adapted to different medical specialties and geographic areas, the authors recommend checking the related legislation and legal aspects of the intended areas where we will implement this tool.

Different sub-specialties can implement the described method to be translated into significant areas of medicine, as the authors can adjust the deployment and execution for the assessment in peripheral areas, conflict zones and other public health crises that require a faster response. This is necessary, as the amount of training to which VS trainees are exposed is low. A simulated exercise offers a novel opportunity to enhance their current diagnostic skills using ultrasound in a controlled environment.

Evaluating and assessing patients with limited access to vascular medicine and other specialties can decrease the burden of vascular disease and related complications and increase the number of treatments available for remote communities.

It is essential to assess the most significant number of patients and treat them according to their triage designation. This management is similar to assessment in remote areas without access to a proper VS consult. The authors were able to determine, classify and redirect to therapeutic interventions the patients with positive findings in remote areas with a fast deployment methodology in VS.

Access to health care is limited due to multiple barriers and the assessment and response, especially in peripheral areas that require a highly skilled team of medical professionals and related equipment. The authors tested a novel mobile assessment tool for remote and conflict areas in a rural zone of Colombia.

In the wake of the COVID-19 pandemic, remote working (RW) has emerged as a viable alternative to working employees in general and knowledge workers in particular. However, previous researchers have worked on the concept, development and facilitation of RW since the 1970s. Therefore, this study aims to review the existing literature on RW to ascertain the evolution of the concept in the business and management domain and provide for requisite arguments to extend the settings for future research agendas.

The authors based this study on a bibliometric analysis of articles (n = 349) retrieved from the Web of Science database published between January 1990 and October 2021. The authors have used a bibliometric toolbox comprising performance analysis, science mapping and network analysis in various software namely, VOSviewer, Gephi and Biblioshiny package in R.

The study’s results accentuated important themes like work–life balance, strengthening digital infrastructure, performance and productivity, hybrid work models and well-being and clustered them under four heads with proposed future research questions.

The study is based on a single database; the authors have used an extensive but not exhaustive list of keywords to retrieve the articles. The analysis employs certain threshold limits while using the science mapping technique.

This study would enable managers and academics to comprehensively understand remote work and offer logical implications to appreciate its nuances.

This study is unique as it recognizes the intellectual structure in the existing literature on RW and traces the advancements and exponential growth post-COVID-19. The authors recapitulated the literature as network analysis of the RW facilitation model comprising the antecedents, outcomes, mediators and moderators.

The purpose of this review paper was identifying barriers to the use of telemedicine systems in primary health-care individual level among professionals.

This study used Scopus and PubMed databases for scientific records identification. A systematic review of the literature structured by PRISMA guidelines was conducted on 37 included papers published between 2009 and 2019. A qualitative approach was used to synthesize insights into using telemedicine by primary care professionals.

Three barriers were identified and classified: system quality, data quality and service quality barriers. System complexity in terms of usability, system unreliability, security and privacy concerns, lack of integration and inflexibility of systems-in-use are related to system quality. Data quality barriers are data inaccuracy, data timeliness issues, data conciseness concerns and lack of data uniqueness. Finally, service reliability concerns, lack of technical support and lack of user training have been categorized as service quality barriers.

This review identified and mapped emerging themes of barriers to the use of telemedicine systems. This paper also through a new conceptualization of telemedicine use from perspectives of the primary care professionals contributes to informatics literature and system usage practices.

The purpose of this paper is to give an overview of artificial intelligence (AI) and other AI-enabled technologies and to describe how COVID-19 affects various industries such as health care, manufacturing, retail, food services, education, media and entertainment, banking and insurance, travel and tourism. Furthermore, the authors discuss the tactics in which information technology is used to implement business strategies to transform businesses and to incentivise the implementation of these technologies in current or future emergency situations.

The review provides the rapidly growing literature on the use of smart technology during the current COVID-19 pandemic.

The 127 empirical articles the authors have identified suggest that 39 forms of smart technologies have been used, ranging from artificial intelligence to computer vision technology. Eight different industries have been identified that are using these technologies, primarily food services and manufacturing. Further, the authors list 40 generalised types of activities that are involved including providing health services, data analysis and communication. To prevent the spread of illness, robots with artificial intelligence are being used to examine patients and give drugs to them. The online execution of teaching practices and simulators have replaced the classroom mode of teaching due to the epidemic. The AI-based Blue-dot algorithm aids in the detection of early warning indications. The AI model detects a patient in respiratory distress based on face detection, face recognition, facial action unit detection, expression recognition, posture, extremity movement analysis, visitation frequency detection, sound pressure detection and light level detection. The above and various other applications are listed throughout the paper.

Research is largely delimited to the area of COVID-19-related studies. Also, bias of selective assessment may be present. In Indian context, advanced technology is yet to be harnessed to its full extent. Also, educational system is yet to be upgraded to add these technologies potential benefits on wider basis.

First, leveraging of insights across various industry sectors to battle the global threat, and smart technology is one of the key takeaways in this field. Second, an integrated framework is recommended for policy making in this area. Lastly, the authors recommend that an internet-based repository should be developed, keeping all the ideas, databases, best practices, dashboard and real-time statistical data.

As the COVID-19 is a relatively recent phenomenon, such a comprehensive review does not exist in the extant literature to the best of the authors’ knowledge. The review is rapidly emerging literature on smart technology use during the current COVID-19 pandemic.

COVID-19 has occurred in more than 150 countries and causes a huge impact on the health of many people. The main purpose of this work is, COVID-19 has occurred in more than 150 countries and causes a huge impact on the health of many people. The COVID-19 diagnosis is required to detect at the beginning stage and special attention should be given to them. The fastest way to detect the COVID-19 infected patients is detecting through radiology and radiography images. The few early studies describe the particular abnormalities of the infected patients in the chest radiograms. Even though some of the challenges occur in concluding the viral infection traces in X-ray images, the convolutional neural network (CNN) can determine the patterns of data between the normal and infected X-rays that increase the detection rate. Therefore, the researchers are focusing on developing a deep learning-based detection model.

The main intention of this proposal is to develop the enhanced lung segmentation and classification of diagnosing the COVID-19. The main processes of the proposed model are image pre-processing, lung segmentation and deep classification. Initially, the image enhancement is performed by contrast enhancement and filtering approaches. Once the image is pre-processed, the optimal lung segmentation is done by the adaptive fuzzy-based region growing (AFRG) technique, in which the constant function for fusion is optimized by the modified deer hunting optimization algorithm (M-DHOA). Further, a well-performing deep learning algorithm termed adaptive CNN (A-CNN) is adopted for performing the classification, in which the hidden neurons are tuned by the proposed DHOA to enhance the detection accuracy. The simulation results illustrate that the proposed model has more possibilities to increase the COVID-19 testing methods on the publicly available data sets.

From the experimental analysis, the accuracy of the proposed M-DHOA–CNN was 5.84%, 5.23%, 6.25% and 8.33% superior to recurrent neural network, neural networks, support vector machine and K-nearest neighbor, respectively. Thus, the segmentation and classification performance of the developed COVID-19 diagnosis by AFRG and A-CNN has outperformed the existing techniques.

This paper adopts the latest optimization algorithm called M-DHOA to improve the performance of lung segmentation and classification in COVID-19 diagnosis using adaptive K-means with region growing fusion and A-CNN. To the best of the authors’ knowledge, this is the first work that uses M-DHOA for improved segmentation and classification steps for increasing the convergence rate of diagnosis.

Working from home (WFH) was one of the major changes that occurred in many organizations during the COVID-19 pandemic. This also led to online training being conducted during this WFH period. The present study investigated the role of technology, manager support and peer support on self-efficacy and job outcomes (i.e. training transfer, work engagement and job satisfaction) of employees while WFH.

The study framework incorporated Bandura's self-efficacy theory. Data were collected from 852 employees in India, and structural equation modeling was used to analyze the data.

The study found positive relationships between ease of technology use, manager support and peer support on self-efficacy and a negative relationship between self-efficacy and technostress. The study also found significant positive relationships between self-efficacy and training transfer, work engagement and job satisfaction. Moreover, the study also identified the moderating effects of WFH and technical issues in the relationships of self-efficacy with training transfer, work engagement and job satisfaction.

The study is novel in that it extended self-efficacy theory regarding the WFH context with influencers such as technology, managers and peers as organizational factors. It also demonstrated the effectiveness of remote working and online training considering the potential antecedents while WFH. Moreover, the study highlighted the simultaneous role of technology and people (managers and peers) in enhancing job outcomes by increasing self-efficacy among employees.

Given the limited literature on depression as a contributing factor to compulsive social media use, the present research examines the role of perceived depressive mood (PDM) in developing compulsive social media use behavior. The authors also identify and hypothesize channels such as contingent self-esteem (CSE), social interaction anxiety (SIA) and fear of negative evaluation (FNE), which may explain how PDM affects compulsive social media use.

The research model was empirically tested with a survey of 367 Chinese university students using structural equation modeling by drawing on the escape and self-presentation lenses.

The findings indicate that PDM contributes to compulsive social media use behavior both directly and indirectly through CSE. Furthermore, the impact of CSE on compulsive social media use is mediated by the FNE, whereas SIA fails to mediate this effect.

The results can advance the authors’ knowledge of the role and process by which depressive mood impacts compulsive social media use. These findings may add insights into psychological treatment and help in, for example, developing counseling programs or coping strategies for depressed people to protect them from using social media excessively.

This research identifies the pathway mechanism between PDM and compulsive use of social media. It also increases the understanding of how CSE and social interaction deficiencies contribute to compulsive social media usage (CSMU).

This study aims to develop a framework that explains how and when telework is related to emotional exhaustion and task performance, by conceiving work overload as a mediator and self-leadership as a moderator. For this purpose, two studies were conducted. Study 1 aims to understand whether telework would be related to emotional exhaustion and task performance and if work overload would mediate such relationships. Study 2 aims to analyze whether self-leadership was a significant moderator of the mediated relations found in Study 1.

The hypotheses were tested in a sample of 207 (in Study 1) and 272 (in Study 2) participants, which were exclusively teleworking. The results were analyzed using PROCESS macro in SPSS.

The results of Study 1 showed that telework dimensions were negatively related to work overload, which consequently decreased emotional exhaustion and increased task performance. In Study 2, self-leadership moderated the indirect effect of work overload on the relationship between telework and emotional exhaustion, such that the indirect effect was stronger for those who scored higher in self-leadership. However, it was not significant for task performance.

This paper adds to research on telework by focusing on the employee's mental health and performance, in the context of mandatory confinement. The authors identified telework dimensions that may act as resources to cope with the increased work overload inherent to telework, as well as the importance of personal resources in these relationships.

The study aims to explore and validate the revised psychological contract scale in this new normal era.

To serve the purpose, four studies were conducted. Study 1 was conducted for item generation through the extant literature review and phenomenological study. Study 2 highlighted the expert review. Study 3 explained the confirmatory factor analysis. At the end of study 3, the new psychological contract content had 14 items along with 15 traditional psychological contract content items. The nomological study validated the scale with the help of antecedent, i.e. supervisor's support, and outcomes, i.e. well-being and innovative behavior.

The revised psychological contract was bifurcated into two categories: new and traditional. Further, the revised psychological contract scale was having two dimensions: content and breach/fulfillment. The new content was the outcome of changes in perceived obligations due to pandemic. The nomological study found that supervisor support had a positive impact on the content of the psychological contract and fulfillment/breach of the psychological contract. Further, it was found that the new content of psychological contract was impacting more on well-being and innovative behavior than the traditional psychological contract.

In the new normal era, the working style and patterns have changed. Thus, it was important to capture changes in perceived obligations and employees' perception regarding to which extent their organizations were able to meet these altered perceived obligations. The study has direct implications for the practitioners as the revised psychological contract scale enlisted the perceived obligations of the employee and the extent to which these obligations were fulfilled by the employer. The study is also helpful in developing new normal HR policies and practices in the organization.

The study is original as it creates a new scale to measure the content of psychological contract and fulfillment/breach of psychological contract during new normal.

Corporate social responsibility (CSR) was mandated and institutionalised in India through the Companies Act (2013), a decade ago. It is critical to understand the priorities of the models used by the companies to effectuate their CSR policy. This paper aims to understand the skewing of interest towards Education and Health interventions. The paper then proposes a framework to cross-level and effectuate CSR programme implementation.

The qualitative study conducted in-depth interviews of the stakeholders from the CSR environment. The findings are used to derive a model to effectuate CSR in India.

The findings may be divided into two sub-themes – (a) observations from the field study and (b) integrated solution ecosystem (ISE) framework. The qualitative study and the insights form the first component. The proposed framework which can enhance the efficiency of CSR practices may be found in the second sub-theme.

Operationalisation of the proposed model, if adopted would require integrated efforts from multiple functional departments which could lead to an extended timeframe for implementation. This may eventually lead to a need to revise the model in the making. The research could also include perspectives from governmental stakeholders which is missing here.

The emerging model can present an opportunity for corporates and policymakers to revisit the CSR structure and frameworks. It can also be used to evaluate and audit the CSR practices of companies.

The ISE posits a bunch of actionable themes which can deliver an impactful transition from the existing approach to CSR to a more far reaching one. While the ground rules are revisited, the approach also allows a critical departure from a corporate-driven model of engagement with the community. The modifications or corrections in this model would also mean a more inclusive layering of developmental interventions. The diversity which could potentially be brought in to designing interventions can be another key impact.

This paper presents insights for some of the pivotal stakeholders of CSR in countries like India. It presents a possible model of effective and optimal utilisation of CSR spending.