Coal mining remains a high-risk occupation in India despite sustained regulatory and technological interventions. This study analyses the long-term trends in fatal and serious accidents in Indian coal mines from 1997 to 2022 suing secondary data from the official sources of the Government of India. Descriptive statistics combined with the Mann- Kendall trend test and Sen’s slope estimator for the assessment of the trend direction and magnitude. The results indicate a statistically significant and consistent decline in both fatal and serious accidents, with a more pronounced reduction in the rate of serious accidents. However, the continued occurrence of accidents highlights persistent occupational safety risks. The findings emphasize the need for continued regulatory enforcement, systematic monitoring and preventive safety strategies for strengthening mining safety performance.
Explosive adoption has transformed trading, risk management, and decision-making in the financial markets by introducing Artificial Intelligence (AI). Financial and regulatory problems are looming large as a result of this shift. To institutions and politicians, pressing concerns now revolve around market manipulation, the transparency of algorithms, the ethical use of AI, and compliance with current financial regulations. The dynamic legal landscape that regulates the use of AI in capital markets, including international regulatory regimes, training paradigms, and issues related to enforcement, is explored in this study. It further places a focus on the role of the financial regulatory authorities in investor protection, market stability, and fostering ethical uses of AI. A regulatory policy that effectively balances innovation and minimizes systemic risks is required, the report suggests.
Nowadays, software process improvement (SPI) is a way for strengthening the productivity and the competitiveness even for small and medium sized organizations, not to mention for large sized ones. However, it is a fact that small and medium sized organizations have limited amounts of the required resources and times to reach high maturity of maturity of models such as CMMI. In addition, there is very little research on quantitative process management approaches, suitable for small and medium sized organizations. In this paper, we propose a simple process improvement model based on quantitative management which might be used by small and medium sized organizations, and show an example applied it for a development team of a small sized organization.
The present study became performed on role conflict among teachers in terms of their expert commitment and work motivation. Role conflict is an anxiety that happens when someone is facing more than one role having more than one status. It means that it occurs between two or more than two roles. Role conflict is something which enables us to exchange our behavior, the battle both it can arise in a quick period of time or it occurs in an extended period of time, and on occasion it occurs with situational reports. The term “role of conflict” cannot be recognized, except we understand the term position. Role is a non-stop system and changes in line with situation. Role conflict arises due to overburdening of roles. When an individual has to play more than two roles, then role conflict arises. The role conflict arises at the identical time when someone has to play more than one role, like that of a teacher. The role of the teacher is to do all-round development of the scholars and make them an crucial part of the society by way of their proper socialization. A teacher ought to do all his actions honestly. They need to be sensitive in the direction of the emotional needs of youngsters and those who need special attention. A teacher ought to have a well-defined self-picture which assists him to perform better and get better effects. These qualities, along with professional abilities of teachers, ensure their commitment and dedication towards the profession at their very best. Motivation has relevance and it is essential to the long-term growth of any educational system.
This study explores the factors influencing Generation Z’s (Gen Z) intention to participate in remote work. This work arrangement has gained considerable popularity due to technological progress and the global move towards flexible working environments, especially following the COVID-19 pandemic. Using the Theory of Planned Behaviour (TPB), this research investigates the role of three key constructs—Attitude Toward Remote Working (ARW), Social Norms (SN), and Perceived Behavioural Control (PBC)—in shaping the Intention to Remote Work (IRW) among Gen Z. A survey was carried out with 159 participants. The data were analysed using SmartPLS, a structural equation modelling (SEM) tool. The findings indicate that Attitude Toward Remote Working (ARW) has the strongest influence on IRW, followed by Perceived Behavioural Control (PBC), while Social Norms (SN) also significantly affect remote work intentions. The study suggests that Gen Z is inclined towards remote work, valuing its flexibility, autonomy, and work-life balance. This research adds to the expanding body of work on remote employment and Gen Z’s preferences, providing valuable insights for organisations seeking to align with this generation’s evolving expectations in the workforce.
The logistics sector is evolving rapidly with the combination of Internet of Things technologies, yet efficient decision-making in supply chain environments remained a critical challenge. The study addressed the issue of real-time visibility, delayed decision response, and inconsistent predictive accuracy in logistics operations. The background of the study was grounded in IoT-enabling tracking systems that continuously generated heterogeneous data across supply chain nodes. However, the raw data streams lacked robust analytical interpretation for actionable intelligence. The problem focused on inefficiencies in predictive logistics routing and inventory forecasting within dynamic supply chain networks. Conventional machine learning models often is limiting adaptability to evolving transportation patterns and demand fluctuations. To overcome these limitations, an Adaptive Gradient-Enhanced Ensemble Learning (AGEL) method is introduced. The proposed method is combined ensemble learning principles with adaptive weight optimization for improving prediction stability. The methodology is utilized IoT sensor data combination, feature normalization, and AGEL-based classification for demand prediction and route optimization. The system is evaluated using standard logistics performance metrics such as delivery time, prediction accuracy, and resource utilization efficiency. The results are showing that the proposed framework is improving prediction accuracy and reduced operational latency that is compared to baseline models. The proposed method is combined ensemble learning with adaptive gradient-based weight optimization. The system is achieving 93% prediction accuracy, 26% delivery time reduction, 91% resource utilization efficiency, 90% route optimization efficiency, and 85 ms system latency. The results is confirming that adaptive learning is significantly improving the logistics performance that is compared to baseline models.
The main aim of this study was to assess service quality and customer satisfaction in Jinka town selected hotels, resort and lodges by applying service quality dimensions of tangibility, reliability, responsiveness, confidence and communication. Descriptive statistics (mean score and standard deviation) were used to examine the customers’ perceptions of each service quality dimensions with respect to the selected hospitality service providers and current status of customer satisfaction. The highest mean score was observed from tangibility dimension whereas the least performance was obtained from the mean score of communication dimension. Correlation and regression analysis were used to see the relationship between dependent and independent variables as well as to investigate cause and effect relationships. The key finding showed that there was a positive and significant effect of service quality dimensions of tangibility, reliability and communication on customer satisfaction and selected hotel, resort and lodge guests status of satisfaction shows that they were slightly or satisfied to some extent with the overall services. The confidence and responsiveness dimension of service quality dimension did not significantly affect guests’ satisfaction. Though, it is not statistically significant, hotelier simply cannot ignore the importance of confidence and responsiveness because literatures supported that they are crucial indicators of customer satisfaction.
Traditional leave management processes in academic and corporate institutions continue to suffer from inefficiencies caused by paper-based workflows, weak validation, inconsistent leave balance tracking, and limited system integration. Although several web-based solutions exist, many lack transactional consistency, enforce minimal business rules, or fail to follow modular architectural principles. This paper presents the design and implementation of a role-based Employee Leave Management System developed using Spring Boot and RESTful architectural principles. The system follows a layered architecture separating controller, service, repository, and data transfer concerns to improve maintainability and extensibility. Core business rules—including non-overlapping leave validation, balance sufficiency checks, controlled status transitions, and role restricted operations—are enforced at the service layer with declarative transaction management ensuring atomicity and data consistency. A key design decision defers leave balance deduction until managerial approval rather than request submission, preventing balance inconsistencies for rejected requests. Functional validation confirms correct enforcement of business rules and transactional behavior across common and edge-case scenarios. The proposed system demonstrates how structured architectural design and transaction-aware workflows can address limitations observed in existing leave management solutions and provides a foundation for further enhancement toward production-ready HR system integration.
The purpose of this study is to learn what students think about NEP2020.The syllabus will undergo significant modifications in NEP-2020, which students must comprehend. In this study, we will examine how students responded to the implementation of NEP and examine the opportunities and difficulties of the program from their perspective. The design for this study was the use of explanatory research, whereby primary data were gathered using a structured questionnaire on a Google form from a total of 180 respondents representing various educational levels and institution types. In the analysis of the students’ perceptions regarding NEP 2020, the impacts of the policy on their learning and skills development, and career readiness, descriptive statistics and chi-square tests were used in IBM SPSS Statistics 20. It was established that the students have moderate awareness regarding NEP 2020, and that most of them regard the policy as positive towards improving education and skill-based learning. On the other hand, the students have access to few information sources and face many challenges in implementation such as lack of awareness, inadequate career guidance, and adaptability. No statistically significant relationship was found between the age of the students and their awareness of NEP 2020 and no significant association between educational level and challenges in implementation.
Water scarcity in semi-arid regions like Rajasthan, India, necessitates management strategies that are both ecologically sound and socially sustainable. Top-down, engineering-focused approaches have often fallen short, prompting a renewed interest in community-led initiatives grounded in traditional wisdom. However, rigorous analysis of how traditional and scientific knowledge systems can be effectively integrated at the local level remains limited. This paper addresses this gap through a community-based participatory research (CBPR) case study of a revitalized johad (a traditional rainwater harvesting structure) in the Karauli district of Rajasthan. The study employs a mixed-methods approach, framing the project within the theoretical lenses of Traditional Ecological Knowledge (TEK) and CBPR. It evaluates the outcomes of community-led conservation interventions by triangulating quantitative, time-series data on key limnological parameters (temperature, pH, dissolved oxygen, turbidity, nitrates) with qualitative data from semi-structured interviews and focus groups with community members. The results reveal distinct seasonal dynamics in the pond’s ecosystem, with a pronounced turbidity spike during the monsoon that is effectively mitigated by community-managed interventions. The triangulated analysis demonstrates a strong congruence between measured improvements in water quality and the community’s own perceived ecological and social benefits. The findings underscore that the success of the initiative stems not just from physical restoration but from the revitalization of the johad as a socio-ecological system, governed by local institutions and informed by TEK. This case study provides a replicable model for water governance, suggesting that national policies like the Jal Jeevan Mission could be enhanced by integrating bottom-up, participatory approaches that empower communities as knowledgeable stewards of their water resources.
This study explores the consumer demand and sales trends in the ghee market from the retailer's perspective. Through a structured survey of 140 retailers across urban and semi-urban areas, the research examines demographic profiles, consumer preferences, purchasing behaviour, and the impact of promotional activities on ghee sales. The findings reveal that most retailers operate convenience stores in urban locations, with a majority being self-owned and having extensive market experience. Consumer preferences for ghee are consistent across age groups and store locations, with no significant influence of health awareness on purchasing decisions. The market demonstrates steady demand, with most stores restocking monthly and selling over 25 kg of ghee per month. Promotional strategies, particularly price discounts and festive offers, significantly influence sales, especially when supported by company incentives. Despite the growing emphasis on health benefits in marketing, customer inquiries about health attributes remain relatively low. The study highlights the importance of quality, branding, and targeted promotions in maintaining and enhancing ghee sales, offering valuable insights for retailers and manufacturers aiming to capitalize on evolving consumer preferences.
India''s push toward digital payments, accelerated by the 2016 demonetisation, has framed Unified Payments Interface adoption as a marker of financial empowerment. Using survey data from 328 rural entrepreneurs in Kerala, this paper suggests that UPI adoption is more accurately understood as a fear-driven protective response. Drawing on Protection Motivation Theory, the study finds that UPI adoption is driven by fear of losing customers, general self-efficacy, and UPI-specific efficacy. Demographic characteristics including gender, age, education, and prior business failure do not affect the UPI adoption. Negative moderation effect of risk-taking ability on the relationship between UPI adoption and revenue growth indicates that lack of solid plan yields less revenue gains. While digital efficacy drives adoption, risk-taking functions as a mediator that weakens the link between self-efficacy and revenue growth. The study offers critical insights for policymakers designing digital inclusion strategies for the rural informal sector.
This study examines the impact of financial technology (FinTech) on financial inclusion in India from 2020 to 2024. Over the past five years, India has witnessed a digital revolution in its financial sector, driven by initiatives like UPI, Aadhaar-linked banking, mobile banking apps, and government schemes such as PMJDY and MUDRA. Using secondary data from government and regulatory sources, this study analyzes trends in digital payments, bank account penetration, access to credit, and rural banking infrastructure. The findings indicate a strong positive correlation between FinTech adoption and increased access to financial services, particularly among rural populations and women. The study concludes that FinTech has become a crucial driver of inclusive financial growth, although certain barriers such as digital literacy and infrastructure gaps still persist. This paper provides insights for policymakers, financial institutions, and stakeholders aiming to build a more inclusive financial ecosystem in the digital age.
The electric vehicle (EV) industry has been significantly mitigating greenhouse gas emissions and addressing climate change by offering a sustainable alternative to fossil fuel-powered vehicles. Environmental concerns, cost savings, government incentives, range anxiety, and brand image were key determinants of consumer decisions (Raju S, et al. 2024) [1]. This study is undertaken to investigate factors affecting customer satisfaction towards EV two-wheeler and explore the relationship between service quality and customer loyalty in EV two-wheeler sector in Telangana. Applying proven models such as SERVQUAL and Oliver’s disconfirmation theory, the research sought insights to enhance customer-centric strategies and inform policymakers and EV businesses. Comprehensive literature review and rigorous methodology examined the interplay between service quality, customer satisfaction, and loyalty. Simple random and convenience sampling methods were applied to collect the responses from the respondents who are using EV-two-wheeler in the Warangal urban district of Telangana State. The Chi-square test (?2) was applied to determine the association between customer satisfaction, loyalty, and service quality parameters. Findings indicated a significant correlation between customer satisfaction, loyalty, and service quality. Key determinants of customer satisfaction and loyalty include employee engagement, service reliability, the appearance of physical facilities and promised product features specifically in terms of mileage concern and life span of battery.
Large Language Models (LLMs) have recently become widely used by private investors seeking personalized financial guidance. Yet emerging research indicates that such models systematically amplify several investment risks, including excessive geographic concentration, sector clustering, trend chasing, elevated active management exposure, and increased total expense ratios. At the same time, Multi-Agent LLM architectures have demonstrated notable improvements in quantitative financial analytics by integrating specialized reasoning components, structured workflows, and dynamic code execution. This paper combines these two streams of insight by introducing a novel Bias-Aware Multi-Agent LLM Framework designed specifically for retail investment advisory. The proposed system incorporates risk auditing, bias detection, regulatorystyle constraint enforcement, and user-centered explanation mechanisms into a Multi-Agent foundation. Experimental evaluations show that architecture substantially reduces key investment risks while preserving clarity, interpretability, and rigorous analytical performance. The work aims to move toward safer, more transparent, and more responsible AI systems for consumer-facing financial applications.
Artificial intelligence (AI) tools are now widely incorporated into HR functions, yet employees often remain uncertain about how fair and transparent these systems truly are. This study explores how professionals in the IT sector interpret fairness in AI-supported HR decisions and how these perceptions influence their trust in AI, readiness to use digital tools, and inclination toward innovation. Survey data from 258 employees in Karnataka were analysed using structural equation modelling, which showed that distributive, procedural, and interactional justice each contribute significantly to building trust in AI systems. Trust emerged as a complete mediator, linking fairness judgements to digital readiness and innovation behaviours. Organizational culture strengthened the trust–readiness link, suggesting that supportive work environments help employees engage more confidently with AI technologies. Latent profile analysis further revealed three distinct employee groups based on fairness and trust levels. The study highlights the importance of transparent communication, fair system design, and culture-building efforts to support human-centred AI adoption in HRM.
AI has a larger role to play in influencing the way information is distributed today, especially among youth in this digital age. Expanding this line of research, examining the impact AI-informed data on knowledge sharing as part of a youth reading culture can help to determine how algorithm-mediated publishing practices shape both content consumption and the enculturation into an information ecosystem. This is in line with a growing fear that the use of AI algorithms to personalize content recommendations could increasingly filter what we read and create knowledge silos. In order to investigate this issue, a quantitative research design was used and the data were collected through a survey questionnaire from 140 respondents within the age range of 18-25. As such, the survey was designed to capture information of reading practices as well as types of content consumed and how AI-curated recommendations affected knowledge acquisition. Using responses as data points, this study investigates whether AI algorithms promote more heterogeneous knowledge or amplify current preferences and reduce the exposure to new diverse perspectives. The study’s results are expected to shed light on the relationships between AI-fed data and youth reading cultures, by examining misinformation in particular as an example of the possible boons and banes behind algorithmic content curation. The researchers hope that the results will inform educators, policymakers and tech developers on AI interventions in either encouraging or destroying knowledge diversity and cultural reading literacy among a younger audience.
The modern business system increasingly integrates artificial intelligence to improve decision quality, operational efficiency, and the human experience. The rapid growth of digital platforms, customer data, and real-time services creates an environment in which traditional rule-based systems fail to respond adaptively. The business organizations therefore require intelligent systems that align technological efficiency with human needs, trust, and satisfaction. Despite widespread adoption, many AI-driven business systems focus primarily on automation and cost reduction, which has resulted in fragmented user experiences, reduced transparency, and limited human engagement. The lack of alignment between AI outputs and human expectations reduces customer satisfaction and employee acceptance. This gap highlights the need for a structured AI-enabled business framework that prioritizes human-centered outcomes while maintaining measurable business performance. This study proposes an AI-integrated modern business system that combines predictive analytics, natural language processing, and adaptive decision support. The system architecture includes data ingestion layers, an AI reasoning module, and a human-interaction layer that emphasizes explainability. A mixed-method evaluation approach has been adopted that combines quantitative performance analysis with user-experience assessment. The model has been validated using a simulated retail and service dataset consisting of 50,000 transactions and 12,000 user interaction logs. The proposed system has achieved a 28.6% improvement in customer satisfaction scores and a 21.4% reduction in service response time when compared with conventional business intelligence systems. Decision accuracy has improved from 76.2% to 89.7%, while employee task efficiency has increased by 18.9%. The explainable AI module has improved user trust ratings from 3.1 to 4.2 on a five-point scale. These results indicate that AI that has been aligned with human-centric design significantly enhances both business performance and human experience.
In today’s swiftly growing and urbanizing world with new technologies and industries every day, the generation of waste is a major concern as the daily waste generation is increasing rapidly. Chennai is one of the most densely populated cities of India, which faces the significant problems of waste generation, collection and treatment of household waste. This study aims to analyse household waste management practices, assess the level of awareness regarding efficient household waste management practices and compare the important provisions of the Solid Waste Management Rules, 2016 and the Solid Waste Management Rules, 2026. Convenience sampling method was used to collect primary data from 120 households from different areas of Chennai city by using Questionnaire. The findings of the study reveals that the most commonly generated household waste among the respondents is plastic waste (82.5%) and on an average 1.33 kg of household waste is generated in a day. Further, among the respondents who segregate household waste, segregation of electronic waste (38.2%), biomedical waste (27.6%) and hazardous waste (26.3%) is considerably lower. Door-to-door corporation collection is the most commonly adopted waste disposal method among the respondents and around 51% of the respondents stated that they give their household waste for recycling. The study also reveals that the mean percentage of awareness on efficient household waste management practices is 77.36 and majority of the respondents (61.7%) fall under high awareness category.
Information and Communication Technologies (ICTs) have transformed various aspects of human life today, business included. In business organisations, ICT possess the potential of enhancing organisational processes and could contribute to their bottom-line performance. However, there is a critical shortage of ICT-related skills in South Africa, a challenge which motivated the present literature review. The objectives of the review were to establish the major ICT skills that are in demand within South African business organisations; to determine the causes of ICT skill shortages in South Africa, and to suggest possible solutions for ICT skills shortage in South African business organisations. A systematic review of the literature was done by searching the Web of Science (WoS) and SCORPUS databases. The PRISMA framework was relied on for question development whilst several inclusion and exclusion criteria were applied. A total of seven (7) articles were included in the review. The major findings were that there is a high demand for soft ICT-related skills in South Africa. Some of the major skills that are needed in the job market are graphic designing, automation, content creation, software development and social networking. The major causes of ICT skills shortage in South Africa are skills mismatches, potential costs of integrating ICTs, country effects and a skills-oriented economy. Also, there are various firm-level factors which can lead to ICT skills shortages, including the type of firm ownership, its level of innovation and informality. The strategies which can be used to address ICT skills shortages include the use of PPPs, training and education and attending to the current ICT-related policies. The research carries implications on ICT policy development because the findings directly map the root causes of ICT skills shortage.
