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This study aims to identify how ICT appeared as an emergent business strategy and to investigate the impact of ICT adoption factors on the perceived benefits of micro, small and medium enterprises (MSMEs).

A total of 393 responses from Indian small and mid-size enterprises (SMEs) were collected for the final analysis. The study presents the partial least-squares structural equation modeling with the Chi-square test and descriptive analysis as a methodology based on numerous independent variables and one dependent variable.

The findings indicate that ICT adoption during and following the COVID-19 pandemic is constant in nature of the enterprise. Moreover, the results indicate that different adoption of ICT factors influence on perceived benefits of organizational performance of Indian MSMEs that lent good support except for the regulatory framework.

The implications of the current research help Indian MSMEs to take investment decisions in various technologies that help the organization. Furthermore, managers and practitioners help the organization in deciding which technology adoption factors are more critical to the betterment of the organization.

The study found certain ICT adoption factors that have a significant role in organizational performance in Indian MSMEs. Moreover, during COVID-19, investigate ICTs' role as a business strategy.

Micro, small and medium enterprises (MSMEs) are facing major difficulties with working capital, need of digitalisation and lack of skilled workforce during this crisis situation. Managing resilience post pandemic is again a huge challenge for MSMEs. Therefore, the main objective of the study is to identify the critical barriers for recovery of MSMEs from the pandemic outbreak and recommending potential solutions for avoiding vulnerabilities.

The most significant barriers, which will impact MSMEs resilience issues are extracted from vast literature review, discussed with nine experts from MSMEs and further categorised into organisational, operational and technical barriers. Best–worst method (BWM) has been used to find the importance rating of barriers for developing resilience in MSMEs.

It can be observed that the organisational barriers (0.507) are the most significant, followed by operational barriers (0.300) and then, technological barriers (0.192). Liquidity crunch and inadequate technical skills of employees are the most significant barriers for MSMEs resilience during COVID-19, whereas fluctuation in input cost, unavailability of containers on time and decreased process efficiency are the least significant barriers for recovering MSMEs post COVID-19.

Findings imply that MSMEs should try to overcome major barriers such as resource constraints, lack of skills and knowledge and inefficient inventory planning.

Findings of study will be of immense use for MSMEs in efficient management of operations and in developing resilience during uncertain business environment.

This paper aims to implement and extend the You Only Live Once (YOLO) algorithm for detection of objects and activities. The advantage of YOLO is that it only runs a neural network once to detect the objects in an image, which is why it is powerful and fast. Cameras are found at many different crossroads and locations, but video processing of the feed through an object detection algorithm allows determining and tracking what is captured. Video Surveillance has many applications such as Car Tracking and tracking of people related to crime prevention. This paper provides exhaustive comparison between the existing methods and proposed method. Proposed method is found to have highest object detection accuracy.

The goal of this research is to develop a deep learning framework to automate the task of analyzing video footage through object detection in images. This framework processes video feed or image frames from CCTV, webcam or a DroidCam, which allows the camera in a mobile phone to be used as a webcam for a laptop. The object detection algorithm, with its model trained on a large data set of images, is able to load in each image given as an input, process the image and determine the categories of the matching objects that it finds. As a proof of concept, this research demonstrates the algorithm on images of several different objects. This research implements and extends the YOLO algorithm for detection of objects and activities. The advantage of YOLO is that it only runs a neural network once to detect the objects in an image, which is why it is powerful and fast. Cameras are found at many different crossroads and locations, but video processing of the feed through an object detection algorithm allows determining and tracking what is captured. For video surveillance of traffic cameras, this has many applications, such as car tracking and person tracking for crime prevention. In this research, the implemented algorithm with the proposed methodology is compared against several different prior existing methods in literature. The proposed method was found to have the highest object detection accuracy for object detection and activity recognition, better than other existing methods.

The results indicate that the proposed deep learning–based model can be implemented in real-time for object detection and activity recognition. The added features of car crash detection, fall detection and social distancing detection can be used to implement a real-time video surveillance system that can help save lives and protect people. Such a real-time video surveillance system could be installed at street and traffic cameras and in CCTV systems. When this system would detect a car crash or a fatal human or pedestrian fall with injury, it can be programmed to send automatic messages to the nearest local police, emergency and fire stations. When this system would detect a social distancing violation, it can be programmed to inform the local authorities or sound an alarm with a warning message to alert the public to maintain their distance and avoid spreading their aerosol particles that may cause the spread of viruses, including the COVID-19 virus.

This paper proposes an improved and augmented version of the YOLOv3 model that has been extended to perform activity recognition, such as car crash detection, human fall detection and social distancing detection. The proposed model is based on a deep learning convolutional neural network model used to detect objects in images. The model is trained using the widely used and publicly available Common Objects in Context data set. The proposed model, being an extension of YOLO, can be implemented for real-time object and activity recognition. The proposed model had higher accuracies for both large-scale and all-scale object detection. This proposed model also exceeded all the other previous methods that were compared in extending and augmenting the object detection to activity recognition. The proposed model resulted in the highest accuracy for car crash detection, fall detection and social distancing detection.

Scholarly works on sex work and sex workers are mostly confined to discourses on human trafficking and the incidence of HIV/STIs among sex workers. Although crucial, this restricted focus has neglected the reality that sex workers are a diverse community, and while their challenges may appear to be linked at first glance, they differ greatly. While extensive research has been conducted on sex workers working in more open settings like brothels, hotels, and streets, there is a scarcity of research on sex workers working in more private spaces, such as, for instance, their own homes. Within the hierarchy of sex workers, home-based sex workers (HBSWs) among the indoor sex workers dominate commercial sex transactions. However, they are often overlooked due to their covert nature and invisible landscape. This chapter addresses the knowledge gap by examining the work lives and conditions of home-based female sex workers (FHBSWs) in Punjab. The study analyzes the complex lives of sex workers who use their home as both a family unit and a workplace. A detailed analysis of the risks and vulnerabilities they face in their daily lives and their coping strategies is also examined in this chapter. The study points out that although working from home may have positive outcomes for sex workers, the integration of sex work into the home environment exposes them to several challenges. Hence, the study emphasizes the need for tailoring interventions for sex workers who operate in different physical environments so that their unique needs and challenges are well addressed.

The COVID-19 health crisis has brought about a set of extra health and safety regulations, and procedures to the construction industry which could influence projects' economic performance (EP). The aim of this paper is to examine the effect of adopting COVID-19 safety protocols on construction sites on the economic performance (EP) of construction projects.

Employing the survey method using a structured questionnaire, data were collected from small- and large-sized construction projects in Nigeria and analysed using partial least squares structural equation modelling (PLS-SEM) technique.

The findings reveal that job re-organization and sanitization have negative significant effects on EP, while social distancing and specific training have no effect on EP. Furthermore, project size moderates the relationship between job re-organization, sanitization, specific training and EP with the stronger effect on the relationships observed in big projects, except for the relationship between sanitization and EP where the moderating relationship is stronger in small projects. However, there is no significant moderating effect of project size on the relationship between social distancing and EP.

As construction project sites continue to operate amidst strict safety protocols, this study offers theoretical and practical insights on how construction projects can adhere to the safety protocols while performing economically.

The originality of this study's findings stems from the fact that it is among the first to provide greater insight on how construction projects have fared economically considering the impact of the various COVID-19 protocols.

The already scarce financial resources coupled with the current COVID-19 pandemic have created the worst scenario for Indian micro, small and medium enterprises (MSMEs). The application of supply chain finance (SCF) solutions to MSMEs can enhance the performance and growth of the sector. But, the implementation of SCF solutions faces various obstacles which restrict the MSMEs' ability to meet their financial requirements. The purpose of this paper is to explore and prioritize the various important barriers hindering SCF application in Indian MSMEs.

Literature on SCF and MSMEs are critically reviewed and barriers affecting the SCF application in Indian MSMEs are scrutinized with the consultation of the experts. The present study applies intuitionistic fuzzy-analytic hierarchy process (IF-AHP) methodology to prioritize the identified barriers and thereafter, the sensitivity analysis is also done to observe the identified barriers under different situations.

The results of the study have revealed that poor cash flow management and working capital management disruption are acting as the most prioritized barriers of SCF. The external factor of cultural challenges has been prioritized as the minimum-influence factor that has least negative influence on the operations of SCF in MSMEs.

The present study bears an important practical and managerial implication to solve real world problems of financial constraints of MSMEs. The managers should emphasize upon the importance smooth flow of cash and working capital management across the supply chains by which better SCF solution can be implemented in MSMEs.

The study conducted is an effort to address the barriers of SCF in Indian MSMEs during the COVID-19 pandemic. The implementation of IF-AHP and sensitivity analysis would help managers and policymakers to comprehend and resolve the prioritized barriers and sub-barriers of SCF in the MSMEs.

COVID-19 has affected the economies adversely from all sides. The sudden halt in production has impacted both the supply and demand sides. It calls for analysis to quantify the impact of the reduction in economic activity on the economy-wide variables so that appropriate steps can be taken. This study aims to evaluate the sensitivity of various sectors of the Indian economy to this dual shock.

The eight-sector open economy general equilibrium Global Trade Analysis Project (GTAP) model has been simulated to evaluate the sector-specific effects of a fall in economic activity due to COVID-19. This model uses an economy-wide accounting framework to quantify the impact of a shock on the given equilibrium economy and report the post-simulation new equilibrium values.

The empirical results state that welfare for the Indian economy falls to the tune of 7.70% due to output shock. Because of demand–supply linkages, it also impacts the inter- and intra-industry flows, demand for factors of production and imports. There is a momentous fall in the demand for factor endowments from all sectors. Among those, the trade-hotel-transport and manufacturing sectors are in the first two positions from the top. The study recommends an immediate revival of the manufacturing and trade-hotel-transport sectors to get the Indian economy back on track.

The present study has modified the existing GTAP model accounting framework through unemployment and output closures to account for the impact of change in sectoral output due to COVID-19 on the level of employment and other macroeconomic variables.

Artificial intelligence (AI)-based human capital management (HCM) software solutions represent a potentially effective way to leverage and streamline a bank’s human resources. However, despite the attractiveness of AI-based HCM solutions to improve banks’ effectiveness, to the best of the authors’ knowledge, there are no current studies that identify critical success factors (CSFs) for adopting AI-based HCM in the banking sector. This study aims to fill this gap by investigating CSFs for adopting AI-based HCM software solutions in the banking sector.

Full consistency method methodology and technology–organization–environment, economic and human framework are used for categorizing and ranking CSFs.

The study identifies the technological and environmental dimensions as the most and least important dimensions for AI-based HCM adoption in banks. Among specific CSFs, compatible technology facilities, sufficient privacy and security and relative advantages of technology over competing technologies were identified as the most important. Implementation of AI-based HCM solutions requires significant outlays of resources, both human and financial, for banks.

The study provides bank administrators a set of objective parameters and criterion to evaluate the feasibility of adopting a particular AI-based HCM solution in banks.