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Perceptual gap among corporate world, academics and students: Personal qualities and employability competencies of students
Personal qualities and employability competencies influence how an individual interacts with others. Employers value employability skills because they are linked to how employees get along with co-workers and customers, job performance, and career success of the employee. Hence personal qualities and employability competencies are considered as one of the essential components for an individuals career development. This study aims to understand the perceptual gap among the corporate world, business school academics and business school students. This study is quantitative in nature and primary data was collected through survey method. The primary data was collected from 377 Master of Business Administration (MBA) students, 276 Business School faculties and 98 managers representing 100 different companies in Bangalore, India. Three different questionnaires were prepared for three groups. All three sets of respondents were asked to rate their perception towards the requirement of personal qualities and skill/competencies required at the workplace in an entry-level job. The study highlights that there is a significant difference in the perception of students, business school faculty and managers towards listed personal qualities and competencies. These perceptual differences result in different types of costs to the company in terms of time, money and energy. The results will help the business schools to develop an innovative business curriculum that can fill the current industry needs. 2019, University of Malaya. All rights reserved. -
Moderating effects of academic involvement in web-based learning management system success: A multigroup analysis
While several educational institutions in India, in accordance to global practices, have adopted Web-Based Learning Management Systems (WLMS) to supplement classroom courses, it is largely seen that these WLMSs fail in their objectives, leading to little or no return on investments. The study aims to define the factors that affect students acceptance of a web-based learning management system and test the moderating effect of their academic involvement in the success of a WLMS. 477 valid questionnaires were collected from university/college students to empirically test the research model using the structural equation modelling approach. The results concludes that indirect and direct effects account for 49% of the variation in the intention to use, which is explained by technical system quality, information quality, educational quality, service quality of the technical support team and user satisfaction. High academic involvement moderates the impact of different service qualities of the WLMS on user satisfaction, intention to use the system, and success of the WLMS. Based on the findings, theoretical and managerial implications are discussed. 2021 -
Assessing perceptions of COVID-19 self-protective measures: a structural equation modeling (SEM) approach
Purpose: This study aimed to develop scales to assess perceptions of coronavirus disease 2019 (COVID-19) self-protective measures (SPMs) and examine the psychometric properties based on the theory of planned behavior (TPB). Design/methodology/approach: A total of 412 participants from Bangalore, India, randomly volunteered to participate in this research. The questionnaire consisted of items related to the TPB scales and demographic details. Findings: A structural equation model showed a reasonable model fit. In total, 70% of the individuals' behavioral intentions of following COVID-19 SPMs were predicted by perceived benefits, barriers, social norms and social influence. Participants' age impacted on perceived benefits and perceived social influence and individuals' behavioral intentions of following COVID-19 SPMs, with a 13.6% difference in model prediction. Originality/value: The TPB can be used as a strong psychometric property to assess behavioral determinants of COVID-19 SPMs. 2021, Sangeeta Mehrolia, Subburaj Alagarsamy and Jeevananda S. -
Will Users Continue Using Banking Chatbots? The Moderating Role of Perceived Risk
AI-powered chatbots have become game-changers for the financial industry. They enable banks to boost customer engagement and improve operational efficiency by lowering the traditional cost of customer support. This study analyses the impact of perceived service quality dimensions on user confirmation, satisfaction and use continuance. The present study also analyses the moderating effect of perceived risk on the relationship between user confirmation, satisfaction and chatbots use continuance. A total of 447 customers, all residing in the Indian city of Bengaluru and having recently used banking chatbot services, were surveyed. Partial least squares structural equation modelling is used to examine the relationships between the variables used in the study. Findings from the study show that all five chatbot service quality dimensions (reliability, interactivity, assurance, responsiveness and understandability) significantly impact the users post-use confirmation, influencing their satisfaction and continuance behaviour. Perceived risk negatively moderates the relationship between user confirmation and user satisfaction. Chatbot service developers and e-service providers can leverage these findings to understand user expectations from chatbots. They will also be helpful to other service sectors, such as insurance, travel and tourism, hospitality and healthcare. 2023 Fortune Institute of International Business. -
Customers response to online food delivery services during COVID-19 outbreak using binary logistic regression
This study aims to empirically measure the distinctive characteristics of customers who did and did not order food through Online Food Delivery services (OFDs) during the COVID-19 outbreak in India. Data are collected from 462 OFDs customers. Binary logistic regression is used to examine the respondents characteristics, such as age, patronage frequency before the lockdown, affective and instrumental beliefs, product involvement and the perceived threat, to examine the significant differences between the two categories of OFDs customers. The binary logistic regression concludes that respondents exhibiting high-perceived threat, less product involvement, less perceived benefit on OFDs and less frequency of online food orders are less likely to order food through OFDs. This study provides specific guidelines to create crisis management strategies. 2020 John Wiley & Sons Ltd -
Inclusion of Sexual Health Education for the Wellbeing and Dignity of Secondary School Children: An Indian Rural Perspective
The study investigates students perspectives on incorporating sexual health education into the curriculum of secondary schools in rural Bangalore. Focused on assessing how such education impacts students physical and psychological well-being, confidence, and ability to make informed decisions, the research collected data from 981 students across 6th to 10th grades. A structured questionnaire, measured on a five-point Likert scale, explored students perceptions of sexual health education and its outcomes. After a meticulous data cleaning process, which included outlier removal, the study utilized a final sample of 900 students. IBM SPSS 25 and AMOS 25 facilitated the statistical analysis. The findings underscore the significant positive effect of sexual health education on students confidence levels. It highlights how this form of education aids in maintaining personal hygiene and fosters balanced decision-making skills among students. The studys results advocate for the implementation of sexual health education in schools, emphasizing its role in enhancing student wellbeing and confidence. Additionally, it contributes to defining the scope and framework of a sexual health education curriculum from the students perspective in rural Bangalore schools, aligning educational objectives with the actual needs and perceptions of the student body. 2023 Indian Institute of Health Management Research. -
Hope, Belief in Just World and Trust in Government: An Interaction Amidst Covid-19 Pandemic in India
The outbreak of COVID 19 has brought about changes in all spheres of human life. In the present times of pandemic, human life has suffered not only from physical stresses but also encountered and endured several mental stresses. In recent times people adopted several measures to bring positivity to their life. The present study explores the relationship between- Hope, Belief in Just World, Covid ?19, and Trust in the Government in India, during the Covid-19 Pandemic. Data was collected online from young adults, via Google forms, using the tools- Adult Hope scale, Covid Anxiety scale, Belief in Just world scale, and Trust in Government. Results showed a significant correlation between the three variables. Hope, Belief in Just World, and Trust in government. Regression analysis found these three variables to significantly impact Covid anxiety. Further, Belief in Just World was found to mediate the relationship between Hope and Covid anxiety. During challenging times, it is important to boost mental health in the right direction. Implications have been further discussed in the article. The Author(s) 2023. -
Hope, Belief in Just World and Trust in Government: An Interaction Amidst Covid-19 Pandemic in India
The outbreak of COVID 19 has brought about changes in all spheres of human life. In the present times of pandemic, human life has suffered not only from physical stresses but also encountered and endured several mental stresses. In recent times people adopted several measures to bring positivity to their life. The present study explores the relationship between- Hope, Belief in Just World, Covid ?19, and Trust in the Government in India, during the Covid-19 Pandemic. Data was collected online from young adults, via Google forms, using the tools- Adult Hope scale, Covid Anxiety scale, Belief in Just world scale, and Trust in Government. Results showed a significant correlation between the three variables. Hope, Belief in Just World, and Trust in government. Regression analysis found these three variables to significantly impact Covid anxiety. Further, Belief in Just World was found to mediate the relationship between Hope and Covid anxiety. During challenging times, it is important to boost mental health in the right direction. Implications have been further discussed in the article. The Author(s) 2023 -
CRM Practices in Private Commercial Banks, Influencing Long Term Relationship and Customer Centric Holistic Approach
Purpose: The exigent purpose of the research is to find out whether socio-economic characteristics impress the study on CRM in private banks and to study CRM practices, factors sway long term relationship between customer and banks, and to know CRM as a customer central holistic approach. CRM gaining more attention as it is attracting and retaining the customers. CRM technology is used to organize, mechanized and integration of sales, marketing, support service and technical support (Robertshaw, 1999). There is a tremendous changes in market, innovation of technology, regional integration increasing competition and especially moderating customers. Approach: A well structure questionnaire was recommended for data collection in order to avoid delay, non-response and incompleteness. Respondents were met while they approached the bank. Either before or after their work respondents were appealed to provide the suitable consumer. A total of 220 questionnaires were in the hand and out of this 200 were usable and this forming 91% success rate. Findings: There is a significant variation in socio economic uniqueness except the demography account at different bank branches and all the factors shows high relationship sums account at different banks. The CRM practices ranked by respondents in the rank-wise are providing security of funds, providing greater value for money and transparency in banking services. Factors like customer satisfaction, well developed privacy policy and quick service are influencing better forever relationship between private sector banks and customers. The measurement of CRM a customer centric approach reveals that CRM protects data privacy, establishes and maintains strong relationship and CRM anticipates anticipates needs of customers. Further factors like data privacy, retention of existing customers and establish and maintain strong relationships are the impressing factors of customer centric approach. 2024, Collegium Basilea. All rights reserved. -
Leveraging Financial Data to Optimize Automation: An Industry 4.0 approach
Industry 4.0 is a transformative approach that leverages advanced technologies to enhance business efficiency and productivity. Automation is a crucial aspect of next-generation industry, and leveraging financial data is essential to optimizing the automation process. This chapter discusses the role of financial data in optimizing automation processes using an I-4.0 approach. Financial data is derived from various sources and can be collected through different methods, such as automated data collection, manual entry, or using sensors and Internet of Things (IoT) devices. The integration of these sources can pose challenges for businesses. The chapter outlines techniques for automation optimization, such as machine learning, predictive analytics, and business process reengineering. Optimizing automation using financial data offers various benefits for businesses, including cost savings, improved quality, and increased profitability. However, there are challenges that businesses face in leveraging financial data, including the integration of various data sources and formats and the need for skilled personnel to analyze and interpret the data. The successful implementation of automation and optimization of processes can lead to sustainable growth and enhanced operations, making it crucial for businesses to remain competitive in the I-4.0 era. By leveraging financial data to optimize automation processes, businesses can maximize their potential and drive growth. Overall, this chapter highlights the significance of financial data in automation optimization and provides insights into the benefits and challenges that businesses must consider when leveraging financial data for optimization. 2024 selection and editorial matter, Nidhi Sindhwani, Rohit Anand, A. Shaji George and Digvijay Pandey; individual chapters, the contributors. -
Deep learning approaches to understanding psychological impacts on vulnerable populations
This chapter investigates the psychological effects on vulnerable groups, with a particular emphasis on the relationship between deep learning techniques and the impact of climate. Vulnerable groups confront particular problems, which might lead to negative psychological results. Investigating this complexity is critical to designing effective intervention techniques. Using sophisticated deep learning techniques, this study seeks to find subtle patterns and correlations in a variety of datasets, including psychological markers, socioeconomic characteristics, and climatic variables. The work employs a comprehensive technique that includes deep learning models, feature extraction, and interpretability analysis to untangle complicated relationships. Preliminary findings imply that deep learning approaches might uncover previously unknown links between climate change and psychological effects on vulnerable groups. This insight adds to a more comprehensive understanding of the difficulties. This understanding contributes to a more holistic grasp of the challenges faced by these groups. By including climate-related factors into the deep learning framework, this study hopes to close the gap between environmental impacts and psychological 2024, IGI Global. All rights reserved. -
Enhancing Movie Genre Classification through Emotional Intensity Detection: An Improvised Machine Learning Approach
Movie Genre Classification through Emotion Intensity is a computer vision technique used to identify facial emotion through a sequential neural network model and to get the genre of the movie with it. This paper delves into latest advancements in Emotion Detection, particularly emphasizing neural network models and leveraging face image analysis algorithms for emotion recognition. Grenze Scientific Society, 2024. -
How Are We Surviving the Pandemic, COVID-19?: Perspectives from Hospitality Industry Workers in Australia
The COVID-19 pandemic has been disastrous and has affected the hospitality industry worldwide, and the people working in the sector were impacted immensely. The purpose of this study is to understand the viewpoints of hospitality workers in Australia on how lockdowns have impacted professional and personal well-being. The case study methodology is adopted for this study. Viewpoints from Australian hospitality workers were collected through semi-structured interviews. With the pandemic taking surprising turns with the rise of new infections and in turn new pandemic waves, the industry is facing a constant lurking fear of lockdowns. Changing variants of COVID-19 creates a profound effect on the psychological and personal well-being of the people employed in the hospitality sector. This chapter would reflect upon the viewpoints of hospitality workers in Australia after two years of the COVID-19 crisis. A real-time assessment is required to understand the vulnerability of hospitality industry workers in a developed country. 2023 Priyakrushna Mohanty, Anukrati Sharma, James Kennell and Azizul Hassan. -
Redefining Disease Detection: Innovative Machine Learning and Wearable Sensor Integration
Wearable sensor technology is considered to be one of the fastest growing fields of information and communication technologies and it has revolutionized the healthcare delivery by enabling continuous and real-time physiological monitoring. This research presents a novel approach that allows an early onset disease detection instigated with the prowess of advanced Graph Neural Network (GNNs) matched with the body streams gathered from wearable machines using its implementation technology - Pythonline of programming named Awesome Geometric libraries referred to as Aztec PyTorch. Graph neural networks (GNNs) are especially suitable within the scope of modeling complex relationships among multivariate inputs of the sensors for modeling the temporal and spatial subjacent dependence of the physiological signs with regards to reality. The proposed system analyzes the data acquired from the various wearable sensors such as heart rate, accelerometers and bio sensors, which help in anomaly detection and hence the detection of the patient having cardiovascular, metabolic and neurological diseases. The synergy between innovative deep learning models and sensors as ubiquitous technologies offers great promise to transform the provision of personalised healthcare services and dealing with disease in its early stages. 2025 IEEE. -
Transition to an Empty Nest: A Phenomenological Exploration of Homemaker Mothers of Out-of-State College Students
This phenomenological study explores the lived experiences of Indian homemaker mothers transitioning to an empty nest, focusing on middle-aged mothers emotional, psychological, and cultural dimensions. Five homemaker mothers, aged between 46 and 50, with at least one child attending an out-of-state college in Bengaluru, participated in semi-structured interviews. Data was analysed using Colaizzis descriptive phenomenological method, which revealed 95 minor themes organised into 9 major themes and 4 overarching categories. The findings highlight a complex emotional intersectionality where feelings of pride and joy in their childrens achievements coexisted with sadness, loneliness, and loss. Cultural expectations surrounding motherhood in India, which emphasises maternal self-sacrifice, further emphasised the emotional challenges the participants face. Coping strategies such as spirituality, social support, and technology emerged as key elements in navigating the transition, with participants often using prayer and digital communication to maintain emotional bonds with their children. The study also found that the empty nest transition triggered a redefinition of parental roles and personal identity. Participants with higher education levels were better equipped to embrace this phase as an opportunity for self-growth, while others struggled with their diminished caregiving role. Technology played a dual roleoffering emotional comfort through digital connection and fostering dependency and frustration. Overall, the empty nest experience for Indian homemaker mothers is emotionally challenging and a potential period of personal rediscovery, shaped by cultural norms and the evolving role of family dynamics. This research provides a culturally specific perspective on a largely Western-studied phenomenon, offering insights for further investigation into the changing maternal identity in India. The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025. -
Generative AI in Action: Empirical Case Studies on Startup Innovation and Entrepreneurial Decision-Making
Generative artificial intelligence (AI) is quickly reshaping the world of entrepreneurship and offers startups more opportunities than ever before to innovate and make strategic decisions, as well as to operate more effectively. The chapter is a synthesis of 50 recent academic articles that critically examine how generative AI, specifically large language models and creative automation systems, is transforming the business model design process and venture execution. It discusses two significant directions, one the effect of generative AI in startup innovation of quick product ideation, bespoke customer service and scalable solutions and the other the effect that AI will have on entrepreneurial decision-making, with AI-based analytics and support systems informing resource allocation and market perspective. Based on empirical evidence and practical case studies, the chapter offers practical recommendations to successful adoption and sets the research directions in the future to allow the full implementation of the transformative abilities of generative AI in the startup world. 2026 by IGI Global Scientific Publishing. All rights reserved. -
DAWM: Cost-Aware Asset Claim Analysis Approach on Big Data Analytic Computation Model for Cloud Data Centre
The heterogeneous resource-required application tasks increase the cloud service provider (CSP) energy cost and revenue by providing demand resources. Enhancing CSP profit and preserving energy cost is a challenging task. Most of the existing approaches consider task deadline violation rate rather than performance cost and server size ratio during profit estimation, which impacts CSP revenue and causes high service cost. To address this issue, we develop two algorithms for profit maximization and adequate service reliability. First, a belief propagation-influenced cost-aware asset scheduling approach is derived based on the data analytic weight measurement (DAWM) model for effective performance and server size optimization. Second, the multiobjective heuristic user service demand (MHUSD) approach is formulated based on the CPS profit estimation model and the user service demand (USD) model with dynamic acyclic graph (DAG) phenomena for adequate service reliability. The DAWM model classifies prominent servers to preserve the server resource usage and cost during an effective resource slicing process by considering each machine execution factor (remaining energy, energy and service cost, workload execution rate, service deadline violation rate, cloud server configuration (CSC), service requirement rate, and service level agreement violation (SLAV) penalty rate). The MHUSD algorithm measures the user demand service rate and cost based on the USD and CSP profit estimation models by considering service demand weight, tenant cost, and energy cost. The simulation results show that the proposed system has accomplished the average revenue gain of 35%, cost of 51%, and profit of 39% than the state-of-the-art approaches. 2021 M. S. Mekala et al. -
Applications of brain-computer interfaces in automated financial services
The purpose of this study is to evaluate the possibility that brain-computer interfaces (BCIs) could bring about a revolutionary transformation in the realm of automated financial services. Brain-computer interfaces (BCIs) hold the promise of revolutionizing the way financial transactions are carried out, increasing security measures, and bodying stoner gestures. This is to be accomplished by providing direct communication between the brain and external bias. Among the subjects that are covered in this study are verification methods, real-time decision-making, and client participation in financial services. The study delves into the intricate workings of BCIs. Through the use of neural data, brain-computer interfaces (BCIs) can supply an unknown position of intelligence into the gestures and preferences of stoners. Because of this, financial institutions can offer services that are more effective and more efficiently adapted to the specific needs of each client. This inquiry emphasizes key breakthroughs in BCI technology. 2025, IGI Global Scientific Publishing. -
Nonlinear analysis of the effect of viscoelasticity on ferroconvection
Thispaper concerns a nonlinear analysis of the effects of viscoelasticity on convection in ferroliquids. We consider the Oldroyd model for the constitutive equation of the liquid. The linear stability analysis yields the critical value of the Rayleigh number for the onset of oscillatory convection in Maxwell and Jeffrey ferroliquids. The use of a minimal mode double Fourier series in the nonlinear perturbation equations yields a KhayatLorenz model for the ferromagnetic liquid, and that is scaled further to get the classical Lorenz model as a limiting case. The scaled KhayatLorenz model thus obtained is solved numerically and the solution is used to compute the time-dependent Nusselt number, which quantifies the heat transport. The results are analyzed for the dependence of the time-averaged Nusselt number on different parameters. 2021 Wiley Periodicals LLC -
Brain Tumor Detectin Using Deep Learning Model
Brain tumor is a life-threatening disease that can disrupt normal brain functioning and have a significant impact on a patient's quality of life. Early detection and diagnosis are crucial for effective treatment. In recent years, deep learning techniques for image analysis and detection have played a vital role in the medical field, supplying more accurate and reliable results. Segmentation, the process of distinguishing between normal and abnormal brain cells or tissues, is a critical step in the detection of brain tumors. In this research, we aim to investigate various techniques for brain tumor detection and segmentation using Magnetic Resonance Imaging (MRI) images. The detection process begins by analyzing the symmetric and asymmetric shape of the brain to identify abnormalities. We will then classify the cells as either Tumored or non-Tumored. This research is aimed at finding a more accurate and efficient method for detecting brain tumors. Four Keras models are compared side by side to find out the best deep learning model for providing a suitable outcome. The models are ResNet50, DenseNet201, Inception V3 and MobileNet. These models gave training accuracy of 85.30%, 78%, 78%, and 77.12% respectively. 2023 IEEE.