Search results

Principally, this study aims to test a conceptual framework of the moderating influence of fear of COVID-19 on the following hypothesized relationships (1) quality of work life and bankers' commitment, (2) the mediating spillover effect of job satisfaction in the quality of work life (QWL) and affective commitment relationship.

A quantitative cross-sectional research design is adopted on 318 bankers chosen from four prominent Indian cities. The mediation model is tested through SPSS, PROCESS macro, and AMOS. Conditional process modeling is also administered to test the moderating effect of fear of COVID-19.

The results suggest that the positive effect of QWL on commitment is completely mediated through job satisfaction. Further, the fear induced by COVID-19 negatively moderated the positive direct relation of QWL with commitment and the positive mediating spillover effect of job satisfaction.

The present research is virtually the first to introduce fear of COVID-19 as a psychological construct, to test a moderated mediation model for implications to organizational behavior and human psychology theory and practice. In coalescence of the need satisfaction, spillover, and COR theories, the authors postulate that as spillover between the domains of an individual's life (work, social, financial, personal, and overall life satisfaction) occurs, such effect is calibrated (augmented or attenuated) by the degree of risk/threat/depletion of their resources in the quest for attaining higher valued resources (overall life satisfaction). The moderated mediation mechanism is suggested for replication in other avenues for greater generalizability.

This study aims to examine which organisational and other factors can facilitate the adoption of artificial intelligence (AI) in Indian management institutes and their interrelationship.

To determine the factors influencing AI adoption, a synthesis-based examination of the literature was used. The interpretative structural modelling (ISM) method is used to determine the most effective factors among the identified ones and the inter-relationship among the factors, while the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method is used to analyse the cause-and-effect relationships among the factors in a quantitative manner. The approaches used in the analysis aid in understanding the relationship among the factors affecting AI adoption in management institutes of India.

This study concludes that leadership support plays the most significant role in the adoption of AI in Indian management institutes. The results from the DEMATEL analysis also confirmed the findings from the ISM and Matrice d’ Impacts croises- multiplication applique and classment (MICMAC) analyses. Remarkably, no linkage factor (unstable one) was reported in the research. Leadership support, technological context, financial consideration, organizational context and human resource readiness are reported as independent factors.

This study provides a listing of the important factors affecting the adoption of AI in Indian management institutes with their structural relationships. The findings provide a deeper insight about AI adoption. The study's societal implications include the delivery of better outcomes by Indian management institutes.

According to the authors, this study is a one-of-a-kind effort that involves the synthesis of several validated models and frameworks and uncovers the key elements and their connections in the adoption of AI in Indian management institutes.

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.

The learning outcomes are as follows: understanding strategic decision-making in EdTech: students will analyse the dynamics of strategic decision-making in the EdTech sector, exploring the rationale behind Unacademy’s shift from online to offline learning; assessing business model transformation: learners will evaluate the challenges and opportunities associated with Unacademy’s transformation from an online-centric model to venturing into physical coaching centres, and this includes considerations of market trends, competition and financial implications; managing competitive dynamics: students will examine the competitive landscape in the Indian EdTech sector, comparing Unacademy’s offline move with industry players, and this objective aims to enhance students’ ability to assess competitive strategies and positioning; strategic response to funding challenges: participants will explore how Unacademy strategically responds to the funding winter, addressing questions of financial stability, organic growth and sustainability in a dynamic market; leadership in uncertain environments: the case aims to develop insights into effective leadership during periods of uncertainty, and students will assess Gaurav Munjal’s leadership decisions and the management team’s role in steering Unacademy through challenges.

These objectives align closely with the case’s focus on strategic management, innovation and business transformation within the context of EdTech, providing students with practical insights and decision-making skills applicable to real-world scenarios.

The case study revolves around Unacademy, a prominent EdTech player in India, undergoing a strategic shift since May 2022. Facing a decline in demand for online education, the company ventured into the offline learning space by establishing physical coaching centres, directly competing with established offline and hybrid players. The case spans the period from the strategic pivot in 2022 to the challenges faced during the funding winter. The protagonist is Gaurav Munjal, the CEO of Unacademy, leading the management team amidst uncertainties.

The case is designed to teach strategic management in the EdTech sector, focusing on the challenges associated with entering the offline education space, particularly without prior experience and amid stiff competition. It explores questions of achieving organic growth, ensuring profitability and making strategic decisions during a funding winter. The industry context is EdTech in India, and the sub-fields of academia include strategic decision-making, business model transformation and competition dynamics within the education sector.

Level and field of study: The case is designed for MBA students with a focus on strategic management, innovation and the EdTech sector. It can also be suitable for executives participating in short courses on business strategy and organizational transformation.

This case is structured for Undergraduate, Postgraduate, MBA and Management Development Programs, aiming to enhance learning in the strategy field through real-world insights and challenges encountered in a dynamic business environment.

Teaching notes are available for educators only.

CSS11: Strategy.

While rapid increase in demand for foods but limited availability of croplands has forced to adopt input-intensive farming practices to increase yield, there are serious long-term ecological implications including degradation of biodiversity. It is increasingly recognised that ensuring agricultural sustainability under the changing climatic conditions requires a change in the production system along with necessary policies and institutional arrangements. In this context, this chapter examines if climate-smart agriculture (CSA) can facilitate adaptation and mitigation practices by improving resource utilisation efficiency in India. Such an attempt has special significance as the existing studies have very limited discussions on three main aspects, viz., resource productivity, adaptation practices and mitigation strategies in a comprehensive manner. Based on insights from the existing studies, this chapter points out that CSA can potentially make significant contribution to enhancing resource productivity, adaptation practices, mitigation strategies and food security, especially among the land-constrained farmers who are highly prone to environmental shocks. In this connection, staggered trench irrigation structure has facilitated rainwater harvesting, local irrigation and livelihood generation in West Bengal. However, it is necessary to revisit the existing approaches to promotion of CSA and dissemination of information on the design of local adaptation strategies. This chapter also proposes a change in the food system from climate-sensitive to CSA through integration of technologies, institutions and policies.

The purpose of this study is to investigate consumers' emotional responses to artificial intelligence (AI) defeating people. Meanwhile, the authors investigate the negative spillover effect of AI defeating people on consumers' attitudes toward AI companies. The authors also try to alleviate this spillover effect.

Using four studies to test the hypotheses. In Study 1, the authors use the fine-tuned Bidirectional Encoder Representations from the Transformers algorithm to run a sentiment analysis to investigate how AI defeating people influences consumers' emotions. In Studies 2 to 4, the authors test the effect of AI defeating people on consumers' attitudes, the mediating effect of negative emotions and the moderating effect of different intentions.

The authors find that AI defeating people increases consumers' negative emotions. In terms of downstream consequences, AI defeating people induces a spillover effect on consumers' unfavorable attitudes toward AI companies. Emphasizing the intention of helping people can effectively mitigate this negative spillover effect.

The authors' findings remind governments, policymakers and AI companies to pay attention to the negative effect of AI defeating people and take reasonable steps to alleviate this negative effect. The authors help consumers rationally understand this phenomenon and correctly control and reduce unnecessary negative emotions in the AI era.

This paper is the first study to examine the adverse effects of AI defeating humans. The authors contribute to research on the dark side of AI, the outcomes of competition matches and the method to analyze emotions in user-generated content (UGC).

This paper reports the panel discussion on the topic of artificial intelligence (AI) and robots in our lives. This discussion was held at the Digitization of the Individual (DOTI) workshop at the International Conference on Information Systems in 2019. Three scholars (in alphabetical order: Ting-Peng Liang, Lionel Robert and Suprateek Sarker) who have done AI- and robot-related research (to varying degrees) were invited to participate in the panel discussion. The panel was moderated by Manuel Trenz.

This paper introduces the topic, chronicles the responses of the three panelists to the questions the workshop chairs posed and summarizes their responses, such that readers can have an overview of research on AI and robots in individuals' lives and insights about future research directions.

The panelists discussed four questions with regard to their research experiences on AI- and robot-related topics. They expressed their viewpoints on the underlying nature, potential and effects of AI in work and personal life domains. They also commented on the ethical dilemmas for research and practice and provided their outlook for future research in these emerging fields.

This paper aggregates the panelists' viewpoints, as expressed at the DOTI workshop. Crucial ethical and theoretical issues related to AI and robots in both work and personal life domains are addressed. Promising research directions to these cutting-edge research fields are also proposed.

Workplace temporalities are being reshaped under globalization. Some scholars argue that work time is becoming more flexible, de-territorializing, and even disappearing. I provide an alternative picture of what is happening to work time by focusing on the customer service call center industry in India. Through case studies of three firms, and interviews with 80 employees, managers, and officials, I show how this industry involves a “reversal” of work time in which organizations and their employees shift their schedules entirely to the night. Rather than liberation from time, workers experience a hyper-management, rigidification, and re-territorialization of temporalities. This temporal order pervades both the physical and virtual tasks of the job, and has consequences for workers’ health, families, future careers, and the wider community of New Delhi. I argue that this trend is prompted by capital mobility within the information economy, expansion of the service sector, and global inequalities of time, and is reflective of an emerging stratification of employment temporalities across lines of the Global North and South.

The objective of the present study is to identify and analyze a set of critical success factors (CSFs) for ice-cream industry [cold chain management (CCM)] that helps in increasing the efficacy, quality, performance and growth of the supply chain organization.

A questionnaire survey with companies in ice-cream sector and a panel study with experts were conducted to identify and validate CSFs and their associated sub-factors. Eight CSFs identified from the cold chain domain vetted for the ice-cream industry and then prioritized by using one of the most well-known decision-making frameworks, Decision-Making Trial and Evaluation Laboratory. The general verdicts of the modelling and its application to the real-world case have been tested through an ice-cream company supply chain.

The result shows that the significant CSFs accountable for the growth of the ice-cream industry are the infrastructure and capacity building, consistent product improvement and operational efficiencies of the value chain. Subsequently, it was identified that the use of IT and related technologies and improved processes for operations also play a considerable role in the performance of ice-cream industry.

The study successfully outlines the effective CCM practices for critical issues. The proposed methodology and factor modelling case demonstration might be useful in analyzing the logistic chains of products such as fruits, drugs and meat.

The meritorious identification of critical areas and executing mitigation plans bring notable benefits to the firms such as improved operational efficiencies, improved time to market performance and product innovation, which bring additional benefits to the producers.

This study aims to present metaGraphos, a crowdsourcing system that aids in the transcription and semantic enhancement of scanned documents by using a pool of volunteers or people willing to participate in exchange for a financial reward.

The metaGraphos can be used in circumstances where optical character recognition fails to produce satisfactory results, semantic tagging or assigning thematic headings to texts is considered necessary or even when ground-truth data has to be collected in raw form.

The system automatically provides a Web-based interface comprising a static HTML page and JavaScript code that displays the scanned images of the document, coupled with the corresponding incomplete texts side by side, allowing users to correct or complete the texts in parallel.

By assisting the parallel transcription and the semantic enhancement of difficult scanned documents, the system further reveals the hidden cultural wealth and aids in knowledge dissemination, a fact that contributes significantly to the academic-scientific dialog and feedback.

Individual researchers, libraries and organizations in general may benefit from the system because it is cost-effective, practical and simple to set up client–server architecture that provides a reliable way to transcribe texts or revise transcriptions on a large scale.