NOLEGEIN- Journal of Information Technology & Management
NOLEGEIN- Journal of Information Technology & Management is a peer reviewed journal and provides a platform to discuss new issues in the area of Information technology issues for management. The journal also seeks to advance the quality of research by publishing papers introducing or elaborating on Information systems planning and policy & Database Management. It's a biannual journal, started in 2018.
MBA Journals (Consortium eLearning Network Pvt Ltd)
Evaluating e-Granthalaya 4.0 Cloud Computing Services in Government Colleges Using ISO2789 and ISO 11620: A Case Study of Raja Shankar Shah University Cluster
Purpose – The purpose of this study was to evaluate the adoption and utilization of the e- Granthalaya 4.0 cloud-based library management system in government colleges under Cluster 3 (Raja Shankar Shah University, Chhindwara), Madhya Pradesh, using standardized international frameworks ISO 2789:2013 and ISO 11620:2014. Design/methodology/approach – This was a case study based on secondary data collected from 48 government college libraries. Library resources, membership, and circulation statistics were analyzed using ISO 2789 indicators, while performance measures such as turnover rate and user engagement were assessed using ISO 11620 indicators. Findings – The findings revealed varied levels of e-Granthalaya 4.0 adoption across libraries. While resource availability was significant in some institutions, circulation and user engagement remained low, reflecting limited utilization of cloud-enabled services. Disparities were observed in holdings per member, circulation per user, and overall collection turnover. Originality/value – This study provides one of the first standards-based evaluations of cloud computing adoption in Indian academic libraries. The findings highlight the importance of capacity-building, digital literacy, and infrastructure support to enhance the effectiveness of cloud-based library automation in higher education.
Cloud computing, e-Granthalaya 4.0, ISO 2789, ISO 11620, Academic libraries, Madhya Pradesh
Copyright (c) 2026 NOLEGEIN- Journal of Information Technology & Management
AI Powered Candidate Matching: Bridging the Gap Between Job Descriptionand Job Seeker Profile
AI-powered candidate matching is becoming one of the most important things in recruitment today because matching job descriptions with candidate profiles is really challenging. Most of the traditional platforms use keyword-based matching which usually fails to understand the real context of skills or job requirements, and because of this a lot of unqualified candidates are suggested while the right ones get ignored which makes hiring inefficient. To solve this, we propose a semantic matching framework which tries to improve matching accuracy by using Sentence-BERT (SBERT) to calculate contextual similarity between job descriptions and candidate profiles by converting the text into dense embeddings and then using cosine similarity to measure compatibility. Also, we apply a TF-IDF based keyword extraction to find missing or underrepresented skills in candidate profiles, which helps in finding skill gaps and giving personalized feedback. Experiments on different job and resume datasets show better precision and clarity than traditional keyword matching. By understanding relationships between professional terms and job roles, the framework connects recruiter expectations with candidate potential. This study also shows that contextual embedding can really improve recruitment intelligence and in future it can include transformer fine-tuning and real-time recommendation features.
Semantic Matching, Sentence-BERT, Cosine Similarity, Job-Profile Compatibility, NLP, Skill Gap Analysis, AI in Recruitment.
Cloud-Native Transformation of an Enterprise Geographic Information SystemPlatform: An Information Technology Management Framework for Scalable,DevOps-Driven Modernization Framework in Utility Operations
Critical legacy geographic information system (GIS) platforms in the electric and gas utility sector are built on monolithic architectures that cannot satisfy the scale, performance, and information management requirements of modern grid operating environments. Conventional GIS systems impose inflexible release cycles, limited horizontal scalability, and brittle point-to-point integrations, preventing real-time geospatial data delivery to control room and field operators. This article presents the design, development, and deployment of a cloud-native enterprise GIS platform as a strategic information technology management initiative at a large North American electric and gas utility. Framed as an information systems planning and policy decision, the proposed platform is built on Amazon Web Services (AWS) cloud infrastructure using a microservices architecture comprising React JS frontend components, Java REST APIs, Docker containers, and Jenkins continuous integration and continuous delivery (CI/CD) pipelines. The four-layer architecture — presentation, application services, integration, and data tiers — supports independent scaling, database modernization, and network security enforcement across each layer. Observed outcomes across deployment efficiency, concurrent user scalability, production incident frequency, and operational decision-making speed demonstrate substantive improvements over the legacy baseline. The article contributes a repeatable IT management framework for regulated utility environments and, more broadly, for mission-critical enterprise information systems requiring high availability, continuous delivery capability, and compliance-aware database governance.
GIS, Cloud-Native Architecture, Information Technology Management, Microservices, DevOps, AWS, Containerization, Database Management, Network Security, CI/CD, Information Systems Planning, Enterprise Cloud Migration.
Smart Libraries and Digital Transformation: Integrating AI, IoT, and Data Analytics for Next-Generation Information Services
The present paper examines the transformative impact of emerging technologies, including Artificial Intelligence, the Internet of Things (IoT), Cloud Computing, and Data Analytics, on contemporary library operations. It explores how the integration of these technologies contributes to the development of smart libraries that enhance accessibility, improve operational efficiency, and deliver personalized, user-centered services. The study highlights the role of automation, real-time data processing, and intelligent systems in streamlining cataloguing, resource management, and information retrieval processes. Furthermore, the paper discusses key challenges associated with the adoption of smart technologies, such as data privacy concerns, high implementation and maintenance costs, and the need for improved digital literacy among both users and library professionals. Through a comprehensive review of existing literature and documented case studies, the research identifies innovative practices and successful strategies that libraries have employed to overcome these barriers. The findings emphasize the importance of continuous technological adaptation and capacity building to ensure inclusive and sustainable knowledge environments. Smart libraries not only support efficient information access but also foster lifelong learning, digital inclusion, and community engagement. Ultimately, the paper concludes that the evolution of libraries into intelligent, technology-driven spaces is essential for meeting the dynamic demands of the modern digital knowledge society.
Smart Libraries, Artificial Intelligence, Internet of Things (IoT), Digital Transformation, Future Libraries, Library Automation.
Application of IoT-Based Sensor Technologies in Agriculture and Industrial Systems for Waste Minimization: A Sustainable Development Perspective
The accelerating depletion of natural resources, growing environmental degradation, and rising levels of industrial and agricultural waste have highlighted the limitations of the traditional linear “take–make–dispose” economic model. As industries and farming systems seek sustainable alternatives, Internet of Things (IoT)- based sensor technologies have emerged as powerful tools for improving resource efficiency and minimizing waste generation. This study examines the application of IoT-enabled sensor systems in agricultural and industrial environments from a sustainable development perspective. In agriculture, precision farming technologies utilizing soil moisture, NPK, temperature, humidity, and climate sensors enable real-time monitoring of field conditions, facilitating optimized use of water, fertilizers, and pesticides while reducing resource wastage and environmental impact. In industrial settings, Industrial Internet of Things (IIoT) technologies, including vibration, acoustic, thermal, and pressure sensors, support predictive maintenance by identifying equipment anomalies before failures occur, thereby extending machinery lifespan, reducing downtime, minimizing material scrap, and lowering electronic waste generation. The study adopts a qualitative secondary research approach based on the analysis of peer-reviewed literature, sustainability reports, and industrial case studies. Findings indicate that sensor-driven data intelligence significantly enhances operational efficiency, resource conservation, and environmental sustainability while supporting circular economy principles and Sustainable Development Goals (SDGs). Despite challenges related to infrastructure, investment costs, interoperability, and cybersecurity, IoT-enabled systems offer a transformative pathway toward sustainable agricultural and industrial development, fostering long-term economic resilience, environmental stewardship, and responsible resource utilization.
Internet of Things (IoT), Sensor Technologies, Precision Agriculture, Industrial IoT (IIoT), Waste Minimization, Sustainable Development, Circular Economy.
Event-Driven Marketing Workflow Orchestration: A Technical Approach to Unified Customer Experiences
Many modern marketing organizations struggle to overcome the market fragmentation of disconnected customer data platforms, campaign engines, content management solutions, and delivery systems. Existing point-to-point integrations and batch-based pipelines are brittle and become an increasing burden as businesses change‚ resulting in delayed campaign delivery‚ inconsistent personalized offers‚ lack of a consistent personalization strategy‚ or divergent eligibility logic across touchpoints. In contrast to system-centric approaches, event-driven architecture enables near real-time response to customer actions by means of standardized, asynchronous publication and consumption of events. This is implemented by a workflow orchestration layer between the specialized systems that implements business intent, as defined by the marketing users, with the help of declarative content artifacts, as executable workflows with consistent decisioning across all activation channels. Building event-driven marketing orchestration systems requires accounting for event schema design choices‚ message streaming infrastructure‚ state management patterns‚ inter-service communication patterns‚ and how to build all this reliably at enterprise scale. Examples of how this impacts the marketing technology stack include increasing campaign velocity‚ increasing personalization precision‚ improving operational efficiency‚ and decreasing engineering maintenance costs. They realize the unified customer experience across web, mobile, app, and email touchpoints, with improved campaign engagement metrics and a competitive advantage in the fast-evolving digital markets.
Event-driven architecture, marketing workflow orchestration, personalization, content management systems, real-time customer activation
AI-Based Career Counselling for Students
Career counselling is a part of helping students figure out what they want to do. This is especially true when they are finishing school and trying to decide what to do next. There are many different paths they can take and it can be really hard to choose. Career counselling helps students with this. Students have to deal with a lot of things like figuring out what they are good at and what they want to do. They also have to think about what jobs available and what they need to do to get those jobs. It is not easy. Artificial Intelligence can really help with this. It uses data to help students find careers that&#39;re a good fit for them. Artificial Intelligence and career counselling can work together to make this process easier for students. Career counselling and Artificial Intelligence are important, for students. Our new plan uses ways to look at all the information we have about a student. This includes what they did in school in the past tests that show what they are good at, what kind of work they want to do and what they like to do. This helps us give each student a plan for what they should do for a career. We are looking at how we can use computers to help students pick a career at three times: when they are in the 10th grade when they are in the 12th grade and when they are, in college. The way we do things combines tools to measure people, standard tests to see what they are good at and computer systems. Like Random Forest classifiers and Neural Networks. To build systems that suggest what career people should have and what they need to learn. We look at how Artificial Intelligence can help fix problems with the way of giving career advice like people interpreting things in their own way not everyone being able to get advice and different people giving different advice by giving guidance that is tailored to each person and based on facts. We use Artificial Intelligence and machine learning architectures, like Random Forest classifiers and Neural Networks to make this happen. Artificial Intelligence helps make career advice. The results show that Artificial Intelligence can really help students feel more confident when making decisions. It can also help reduce the uncertainty they feel about their careers. This means students can make choices about what they want to study and do after school. Artificial Intelligence does this for all kinds of students.
Career Counselling, Artificial Intelligence, Student Guidance, Machine Learning, Career Prediction, template, Scribbr, IEEE, format, NLP, RAISEC, XAI)
AI Powered Candidate Matching: Bridging the Gap Between Job Description and Job Seeker Profile
Human–AI Collaboration in Fraud Detection, Risk Scoring, and Compliance Automation in Fintech
As artificial intelligence (AI) systems assume increasingly consequential roles in financial fraud detection, risk scoring, and regulatory compliance, the question of how human expertise and machine intelligence are best integrated has become central to both operational effectiveness and ethical governance. Human–AI collaboration in fintech is not a transitional state on the path to full automation; it is a principled design choice that combines the pattern recognition and scalability of machine learning with the contextual judgment, ethical reasoning, and accountability that human analysts provide. This article examines how microservices architectures, event-driven platforms, and cloud-native infrastructure support effective human–AI collaboration in fraud detection and compliance workflows. It analyzes collaboration interface design, model explainability requirements, workflow orchestration for mixed human–machine decision pipelines, and the sustainability properties of architectures that minimize wasted computing while ensuring that human attention is directed where it adds the most value. Drawing on verified empirical evidence from recent literature, the article demonstrates that well-designed human–AI collaboration systems achieve fraud detection performance that is substantially superior to either human analysts or AI systems operating independently, while satisfying the fairness, transparency, and auditability requirements that regulators and the public increasingly expect of automated financial decision-making.
Human–AI collaboration, explainable artificial intelligence, fraud detection, risk scoring, compliance automation, fintech, AI governance, model fairness
Assessing Artificial Intelligence Risk Perception Among Generation Z in Delhi NCR
This study examines the risk perception of Artificial Intelligence (AI) among Generation Z in the Delhi National Capital Region (NCR), a demographic that is highly engaged with digital technologies and AI-driven applications. The research aims to identify key concerns, attitudes, and awareness levels related to the societal, ethical, and technological implications of AI. A descriptive research design was adopted, integrating both qualitative and quantitative approaches to ensure a comprehensive analysis. Primary data were collected from 120 respondents through structured questionnaires, supplemented by secondary sources to strengthen the conceptual framework. Analytical tools, including NVivo 11 for qualitative insights and SPSS along with Microsoft Excel for quantitative analysis, were employed to interpret the data. The findings reveal that Generation Z demonstrates a balanced yet cautious outlook toward AI. While respondents acknowledge the potential of AI to enhance decision-making, efficiency, and management of complex systems, significant concerns persist regarding job displacement, algorithmic bias, lack of transparency, data privacy risks, and system reliability. Notably, the lack of transparency in AI algorithms emerged as a major concern, reflecting growing awareness about explainability and accountability. The study underscores the need for robust regulatory frameworks, ethical governance, and increased digital literacy to foster trust and responsible AI adoption. Overall, the research highlights the importance of aligning technological advancements with societal expectations to ensure sustainable and ethical integration of AI.
Artificial intelligence, risk perception, generation Z, data privacy, algorithmic bias, AI ethics, Delhi NCR
Zero-Downtime Modernization of Financial Clearing Engines: A Systematic Review
Financial clearing engines underpin global payment networks, securities settlement platforms, and interbank transaction infrastructures, processing trillions of dollars in financial transactions every day. Despite their critical role, many clearing systems continue to operate on legacy monolithic architectures that are difficult to scale, maintain, and modernize without disrupting essential services. This study presents a systematic literature review of 48 primary research studies published between 2015 and 2026, focusing on architectural approaches that enable zero-downtime modernization while preserving transactional integrity, deterministic ordering, and regulatory compliance. The review identifies five interdependent modernization capabilities that consistently emerge across literature: Strangler Fig decomposition, deterministic append-only execution, micro-batch stream processing, active–active replication, and continuous reconciliation. Together, these patterns support incremental migration from legacy systems to cloud-native architectures without compromising settlement accuracy or operational continuity. Building on the synthesized evidence, the study proposes the Verifiable Migration Mesh (VMM), a control-plane framework that integrates observability, validation, risk assessment, and execution governance throughout the modernization lifecycle. The findings indicate that organizations adopting these architectural strategies can achieve availability levels approaching 99.999%, divergence rates below 0.02%, and throughput improvements ranging from 3.8× to 6× compared with traditional clearing infrastructures. The review also highlights unresolved challenges, including regulatory acceptance of event-sourced ledgers, formal verification across heterogeneous distributed environments, and organizational readiness for large-scale transformation. By consolidating fragmented research across software architecture, distributed systems, and financial technology, this study provides a comprehensive roadmap for advancing zero-downtime modernization as a mature and dependable engineering practice for future financial infrastructures.
Financial clearing engines, zero-downtime modernization, strangler fig pattern, event sourcing, active replication
Strategic Platform Modernization: Best Practices for Migrating Enterprise Applications from Virtual Machines to Cloud-Native Platforms
Enterprise platforms are undergoing rapid transformation from virtual machine (VM)-based infrastructure to cloud-native architectures to meet demands for scalability, agility, and resilience. This migration requires a systematic assessment of organizational readiness and application suitability, coupled with the implementation of technical enablers, including containerization, CI/CD automation, container orchestration, and observability frameworks. This paper introduces three integrated assessment frameworks: a Cloud-Native Maturity Model across five technical dimensions (Application Architecture, Deployment Automation, Observability, Container Standards, and Infrastructure Management), a Migration Readiness Scoring methodology that quantifies individual application suitability (0–100 scale), and a Migration Decision Framework that pairs organizational maturity with application readiness to guide strategy selection across six distinct pathways. The frameworks are validated through pilot assessment methodology with representative applications spanning multiple maturity levels. Beyond assessment, the paper addresses critical technical enablers: application dependency analysis, container image standards, CI/CD pipeline architecture, Kubernetes orchestration patterns, and distributed observability (logs, metrics, traces). These are contextualized within organizational and cultural considerations, including DevOps culture, cross-functional collaboration, and executive leadership alignment. Grounded in CNCF survey data (2024) and validated through pilot assessment case studies, this work provides practitioners with both conceptual frameworks and actionable implementation guidance to achieve higher reliability, faster software delivery, improved operational efficiency, and sustained business agility during platform modernization. Organizations implementing this methodology report a 40–60% reduction in deployment cycle time and a 35–50% improvement in mean time to recovery (MTTR).
Cloud-native architecture, containerization, Kubernetes, CI/CD pipelines, observability, DevOps culture, migration strategy, enterprise modernization
What Does Knowledge Systems Mean: Answers from Literature
The Indian Knowledge System (IKS) constitutes a long-standing and evolving tradition of knowledge production that harmonizes empirical reasoning with philosophical and metaphysical inquiry. Rooted in ancient Indian intellectual practices, IKS presents a comprehensive worldview that integrates Jnan Vignan (systematic knowledge and science) with Jeevan Darshan (a holistic philosophy of life). This paper explores the conceptual foundations and historical development of IKS, tracing its progression from early Vedic literature to its formal recognition and integration within the National Education Policy (NEP) 2020. The study critically examines the epistemological basis of IKS, with particular emphasis on the theory of Pramanas as diverse and valid means of knowledge acquisition. It further analyses the structural organization of IKS through the framework of the 14 Vidyas and 64 Kalas, highlighting their interdisciplinary nature. By comparing IKS with dominant Western knowledge paradigms and other indigenous systems, the paper underscores its distinctive non-dualistic approach, wherein the knower and the known are viewed as interdependent. Empirical relevance and scientific credibility are demonstrated through selected case studies, including advanced metallurgical practices, such as Wootz steel, mathematical innovations of the Kerala School, and principles of sustainable design, embodied in Vastu Shastra. Finally, the paper discusses contemporary approaches to assessing the relevance and continuity of IKS, introduces the notion of cognitive justice, and argues for the system’s potential contribution to addressing modern challenges related to sustainability, ethical development, and mental well-being.
Cognitive justice, epistemology, ethno-science, Indian knowledge system, Pramanas, sustainable development, Vedic mathematics
A COMPARATIVE STUDY OF AI-DRIVEN AND TRADITIONAL APPROACHES TO FINANCIAL COVENANT AND COMPLIANCE MONITORING AT MUFIN GREEN FINANCE
This study looks at how AI-driven financial covenant and compliance monitoring stacks up against traditional methods used by Mufin Green Finance, a leading non-banking financial company in India’s green finance sector. Traditionally, covenant checks depend on manual reviews, periodic audits, and rule-based systems. These processes can be slow, require a lot of labor, and are often delayed by human error. With the rise of machine learning, natural language processing, and predictive analytics, financial institutions can now analyze borrower data in real time. They can identify anomalies early and predict risks rather than just reacting to them later. The research compares both methods based on accuracy, efficiency, scalability, and cost. It also examines practical issues like data quality, model interpretation, and regulatory acceptance. The study is part of a broader change in India’s financial services industry, where NBFCs like Mufin Green Finance are essential for improving access to credit and supporting sustainable development through electric mobility and clean energy financing. By looking at Mufin’s operations, strategic goals, and competitive environment, the project shows how AI- enabled monitoring can improve risk management, support compliance, and strengthen portfolio resilience. The findings add to the ongoing conversation about the role of AI in creating sustainable, transparent, and future-ready financial systems, particularly in emerging markets where green finance is growing quickly.
Artificial Intelligence (AI); Financial Covenant Monitoring; Compliance Management; Non- Banking Financial Companies (NBFCs); Green Finance; Risk Management
Innovating Insurance Lending in Indian Fintech: A Study of Bimapay’s EMI Model, Business Strategy, and Role in Alternative Credit for Insurance Inclusion
This study examines EMI-based insurance lending within India’s fintech ecosystem, focusing on Bimapay Finsure Pvt. Ltd. and its role in improving insurance accessibility through technology-enabled premium financing. Using primary data collected from 41 employees across finance, operations, and technology teams, the research evaluates employee understanding of the EMI model, perceptions of business model sustainability, and attitudes toward AI-driven reconciliation. Statistical analyses – including descriptive analysis, correlation, and regression – reveal that employees generally have positive perceptions of efficiency, accuracy, and the usefulness of AI tools. Correlation results show strong positive relationships between system effectiveness, accuracy, and employee trust in AI (r = .579, p < .001), while regression findings confirm that key variables such as perceived accuracy (VAR00005) significantly predict operational efficiency (p < 0.05). However, certain challenges negatively influence outcomes, such as delays due to manual processes and mismatch-related issues (VAR00007), which showed a significant negative effect (p < 0.05). Descriptive analysis indicates consistent agreement among employees regarding the need for automation and centralized dashboards. Overall, the findings highlight that although Bimapay has adopted several digital tools, gaps remain in reconciliation automation, interdepartmental coordination, and real-time data visibility. The study concludes that enhanced API integration, AI-supported anomaly detection, and unified dashboards are crucial to
Fintech, EMI-based Insurance Lending, Reconciliation Process, Operational Efficiency, InsurTech.
Ethical and Policy Gaps for Responsible AI in Indian Healthcare: Review Toward Viksit Bharat
The accelerated adoption of Artificial Intelligence (AI) within the Indian healthcare sector – particularly in medical imaging, clinical decision support, and predictive diagnostics – has emerged as a critical enabler of the national vision of Viksit Bharat (Developed India). AI-driven technologies offer substantial potential to improve diagnostic accuracy, optimize healthcare delivery, and address systemic challenges such as workforce shortages and regional disparities. This review critically examines existing academic literature, government reports, and publicly available policy documents to assess the ethical, regulatory, and governance frameworks shaping Health AI implementation in India. While the country leverages robust Digital Public Infrastructure (DPI) to support large-scale digital health initiatives, the analysis reveals notable policy and regulatory deficiencies. Foremost among these is the absence of a comprehensive, standalone national Health AI policy that clearly defines accountability, liability, and governance mechanisms. Significant gaps persist in mandating independent audits to identify and mitigate algorithmic bias, particularly in high-stakes diagnostic applications. Furthermore, existing policies inadequately address data protection, transparency, and informed consent within DPI-enabled AI systems, raising concerns regarding patient privacy and trust. The study underscores the urgent need for a coherent policy framework that integrates ethical safeguards, legal clarity, and technical oversight. Addressing these shortcomings is essential to ensure that AI adoption in Indian healthcare remains reliable, equitable, and socially responsible, while effectively contributing to national development objectives and public health outcomes.
Data privacy, Indian healthcare policy, responsible AI, trustworthiness, Viksit Bharat
Scientists’ Perspectives on Artificial Intelligence (AI) in Knowledge Resource Centre Services of the Council of Scientific and Industrial Research (CSIR) Laboratories in Uttar Pradesh, India
This study examines scientists’ perceptions regarding the adoption and use of Artificial Intelligence (AI) tools within the Knowledge Resource Centres (KRCs) of CSIR laboratories located in Uttar Pradesh, India. A structured survey methodology was employed to gather data from scientists working at CSIR-CDRI, CSIR-CIMAP, CSIR-IITR, and CSIR-NBRI. The findings indicate that most respondents are familiar with AI-based applications and actively utilize them for various research-related activities, including literature searching, plagiarism detection, language refinement, reference management, and data organization. Despite this widespread awareness and usage, the level of satisfaction with AI- enabled services varies across laboratories. Key challenges identified include insufficient hands-on training, restricted access to licensed AI tools, technical limitations, and inconsistent institutional support. Some respondents also highlighted concerns about data privacy and the reliability of AI- generated outputs. Nevertheless, the overall perception toward AI integration in KRC services remains largely positive. Scientists believe that AI has significant potential to enhance research productivity, streamline information retrieval, and modernize library services. The study concludes that for effective implementation, KRCs must invest in continuous capacity-building programs, ensure equitable access to advanced AI tools, and strengthen technical and administrative support systems. Such measures will enable optimal utilization of AI technologies, thereby improve scientific research outcomes and advancing knowledge management practices within CSIR institutions.
Artificial intelligence, knowledge resource centres, academic libraries, CSIR laboratories, scientists, user awareness, AI-based tools, research support services
Evolving Paradigms of Research Data Management (RDM): A Conceptual Framework for University Libraries in Rajasthan
The challenge of managing vast amounts of research data is relatively new to Indian universities and is often tied to the issues of open science and data-intensive scholarly research. The present study focuses on the shifts relating to the Research Data Management (RDM) process within the context of higher education systems of the university in the Indian state of Rajasthan. I propose the Integrated RDM Readiness and Stewardship Framework (IRRS-Framework) to fully address these gaps. The IRRS-Framework is a multi-faceted optimistic model of RDM governance within policy, global perspectives, and cross-national infrastructure which includes institutional and ethical RDM stewardship, as well as the paradigms of human governance and capacity. The stewards of IRRS- Framework maintain RDM policy interoperability, and governance and ethics RDM policy alignment on the diverse resource universities of Rajasthan. RDM is often perceived as a technological problem, however, I strongly argue that success stems from holistic shifts within the institute. This paper argues that infrastructure development, capacity building, and governance and ethical accountability pathways emerging from India’s regional university framework and data policy can help drive systemic institutional shifts. RDM ecosystems shaped by institutional policy and governance in Rajasthan’s universities are locally grounded, ethically responsive, and globally aligned.
Conceptual framework, data stewardship, higher education, institutional governance, Rajasthan, research data management, university libraries
Innovative Teaching–Learning Practices in Computer Science
Advances in digital technologies, rapid innovation in the information technology industry, changing learner expectations, and the growing emphasis on higher-order thinking skills have significantly transformed the goals and practices of Computer Science education. Traditional lecture-centric teaching approaches, where students primarily act as passive recipients of information, are increasingly inadequate for preparing learners to address real-world, ill-structured, and complex technological problems. In today’s dynamic digital environment, Computer Science graduates are expected not only to possess sound theoretical knowledge and programming proficiency but also to demonstrate the ability to design, analyze, implement, and maintain complex systems, work effectively in collaborative teams, communicate ideas clearly, and continuously adapt to evolving technologies. These evolving expectations have created a strong need for innovative teaching–learning practices that shift the focus from one-way knowledge transmission to student-centered, interactive, and experiential learning environments. Such practices encourage learners to actively engage with content, apply concepts to practical scenarios, reflect on their learning processes, and develop problem-solving and critical-thinking skills. Active learning, project-based learning, flipped classrooms, and technology- enabled instruction play a crucial role in fostering deeper understanding and sustained engagement in Computer Science courses . In this context, the present paper explores innovative teaching–learning practices in the Computer Science stream, with a particular focus on pedagogical models, technology integration, active learning strategies, assessment innovations, and emerging educational trends. By examining these approaches, the paper aims to highlight how innovative practices can enrich teaching, enhance student motivation and participation, and ultimately improve learning outcomes, thereby aligning Computer Science education with the demands of the modern digital economy
Active learning, Computer Science, flipped classroom, higher-order thinking, innovative teaching, learning outcomes, project-based learning, technology integration.
Management of Technology: Enhancing Organizational Efficiency and Innovation
The rapid evolution of technology has transformed modern business operations, making the management of technology (MOT) a crucial function in organizations. MOT involves the strategic implementation, oversight, and optimization of technology tools to enhance efficiency, improve decision-making, and mitigate risks. This paper explores the key aspects of MOT, including its benefits, commonly used technology tools, essential skills required for success, and career opportunities in the field. Effective MOT enables businesses to streamline operations, enhance internal communication, and optimize resource management. As industries continue to adopt new technologies, professionals with expertise in MOT are in high demand. The findings of this paper underscore the growing importance of technology management and the skills necessary for professionals to succeed in this ever-evolving field. The paper highlights the key competencies necessary for successful technology management, including strategic insight, proficiency in data analytics, project execution skills, cybersecurity knowledge, and the ability to collaborate across departments. As technology becomes increasingly integrated into core business processes, these skills are in growing demand among employers. Additionally, the study outlines the diverse career paths within the field of MOT, featuring positions such as technology manager, IT strategist, chief innovation officer, and digital transformation specialist. With industries rapidly advancing through digitalization and automation, the need for qualified MOT professionals is expected to rise significantly. The research emphasizes the critical role of technological fluency, strong leadership, and adaptability in staying relevant within a constantly evolving digital environment. In essence, proficient technology management not only enhances business performance but also contributes to long-term sustainability and competitive advantage
Copyright (c) 2025 NOLEGEIN- Journal of Information Technology & Management
Impact of Cloud-Based Computing on Human Resource Enactment: An Analysis Using Andhra Pradesh and Telangana It Companies
Human Resource Management (HRM) has undergone major changes in recent years. The emphasis has moved as of the logistical managing activities to being a tactical associate in the whole company approach, mostly through good encouragement for the advancement of information technology in this field of expertise. The expanded usage of information technology has a major influence today on how HRM is handled. This spurred a significant change in processes and activities in Human Resources (HR) within organizations, including how they capture, store, utilize, and exchange knowledge. Many HRM systems have been more effective, and the effect of this service level improvement has allowed HR to be more active in the company plan. A current position in the corporate plan brings substantial improvements to the work of HR and its experts. The digitization of candidates' profiles to do their contour segmentation as per the organizational effort is well achieved by the innovative IT infrastructure i.e. Cloud-Based Computing (CBC). Several kinds of research show that most businesses allow select applicants to introduce the Cloud. Yet most on-site practice inheritance. Video conferencing is one of the recruiting methods common with CBC. The study examines the Impact of Cloud-Based Computing on Human Resource Enactment: an analysis using Andhra Pradesh and Telangana IT Companies.
Human–AI Collaboration in Organizations: Bridging Human Intuition and Machine Intelligence
The combination of humans working with AI systems has become an essential transformation method. All business areas including health care and military operations alongside academic environments are transforming because of human-AI system cooperation. Organizational teams receive previously impossible organizational capabilities from artificial intelligence although these teams face difficulties in deriving sustainable business value from such opportunities. The paper uses findings from multi-disciplinary studies to build practical applications which enable effective human-AI collaborative work. The paper shows that three core elements embrace user strategic alignment and ethical technology development and intelligence sharing which support self-adapting organizational structures. Through human input enabled explainable interfaces and interactive systems healthcare organizations together with defence forces establish trust which leads to enhanced operational performance as studies demonstrate. This framework combines various human operation levels and decision authorities through a systematic construct. Through this framework users acquire superior control capabilities along with increased empowered capabilities and mission alignment and ethical design features in adaptive systems. The paper establishes both scientific advances and develops practical solutions that boost real-time human-machine teamwork effectiveness. Obstacles such as lack of trust in AI-driven decisions, difficulty in interpreting machine-generated outputs, challenges in incorporating AI into current organizational processes, and concerns about maintaining ethical standards continue to limit the full potential of artificial intelligence. To address these issues, this paper synthesizes findings from diverse disciplines—including cognitive science, computer science, organizational theory, and ethics—to present a robust framework that promotes successful human-AI collaboration. The framework emphasizes three essential pillars for effective integration: alignment of AI systems with user strategies, the development of ethically responsible technologies, and the seamless exchange of information between humans and machines. Together, these components enable the formation of adaptive organizational structures capable of responding to the complexities of rapidly changing environments.
A Study on Household Women’s Preference Towards Basic Computer Literacy in Gudalur Town
In today’s digital world, basic computer literacy is essential for social, educational, and economic development. However, many household women, especially in semi-urban and rural areas, face challenges in accessing digital education due to financial constraints, time limitations, lack of awareness, and societal factors. This study aims to analyze the preferences, awareness, and barriers faced by household women in Gudalur Town regarding basic computer literacy. The purpose of the study is to assess their existing knowledge, understand their interest in learning digital skills, identify the challenges preventing them from acquiring computer education, and provide recommendations for accessible and effective training programs. To ensure a thorough grasp of the subject, the study will use both primary and secondary data collection approaches. Surveys, structured questionnaires, and interviews with domestic women will be used to gather primary data in order to examine their learning requirements and difficulties. To bolster the findings and offer a more comprehensive view of developments in digital literacy, secondary data will be collected from government reports, academic papers, and statistics sources. By tackling these issues, the study hopes to support women's digital inclusion and empowerment, which will ultimately increase their economic possibilities and independence. The study’s outcomes are anticipated to reveal shortcomings in existing digital literacy programs and offer practical suggestions for policymakers, educators, and non-governmental organizations to improve their reach and impact. Ultimately, this research aims to support the empowerment of women by fostering digital inclusion, which can enhance their independence, enable access to online resources, encourage active involvement in the digital economy, and elevate their overall standard of living
Quantifying the Impact of Perceived Inclusion on Organizational Citizenship Behavior Using Statistical and Machine Learning Approaches in the IT Industry
Workplace productivity along with collaboration levels increase through Organizational Citizenship Behavior (OCB) as a major organizational factor. Organizations need to comprehend what motivates IT professionals to practice OCB because this understanding must exist to create an all-inclusive supportive work environment. Traditional statistical models cannot detect the complex interactions existing between the important predictors of OCB that include perceived inclusion and psychological safety and cultural orientation although previous studies identified these elements as essential predictors. This research presents an HSML Model which joins SEM for hypothesis testing and ML for feature selection and DNN for predictive analysis to overcome previous analytical limitations. The research gathered information from 500 IT practitioners who worked in international subsidiaries and local branches through an organized selection process. The research utilized SEM for establishing causal relationships between variables while evaluating feature importance through Random Forest and SHAP when analyzing perceived inclusion and psychological safety and cultural orientation and OCB. Predictive accuracy improved when the selected features were used to train a DNN. The HSML model delivers superior performance compared to conventional approaches because it reaches an R² value of 0.85 alongside a classification accuracy level of 93.1% while outdoing multiple linear regression, random forest as well as standalone deep learning models. Analysis of feature importance reveals perceived inclusion (0.42) together with psychological safety (0.34) to be the most influential predictors of OCB. The research establishes that included workplaces with safe psychological environments drive greater employee engagement outcomes. The HSML model functions as a real-time operational tool that provides useful knowledge for HR managers to enhance workplace inclusivity and employee health.
To Study The Impact of Social Media Use On Academic Performance Among College Students In Delhi NCR
Social media has become an integral part of the daily lives of youths, influencing various aspects of their behavior, including study patterns. This executive summary provides an overview of the impact of social media on the study habits and academic performance of college students. Access to Information: Social media platforms provide a vast pool of educational resources, enabling students to access information beyond traditional classroom settings. Collaborative Learning: Youths engage in collaborative learning through social media platforms, fostering group discussions and knowledge-sharing. Distraction and Procrastination: Social media often serves as a major distraction, leading to procrastination and reduced focus on academic tasks. Sleep Disruption: Prolonged use of social media, especially before bedtime, can disrupt sleep patterns, impacting students' overall well-being and academic performance. Comparison and Stress: Youths may experience stress due to constant comparison with peers on social media, leading to feelings of inadequacy and impacting their self-esteem. Fear of Missing Out (FOMO): Fear of missing out on social events and activities shared on social media can contribute to increased anxiety levels, diverting attention from academic responsibilities. Excessive Screen Time: Spending prolonged hours on social media may contribute to reduced study time, affecting youths' ability to manage their academic workload effectively. Multitasking Issues: The constant switching between social media and academic tasks may hinder effective multitasking, leading to lower productivity and academic performance. Digital Literacy Education: Incorporate digital literacy education into academic curricula to help students navigate social media responsibly. Time Management Skills: Emphasize the importance of effective time management to balance social media use and academic responsibilities.
Factors Impacting the Uptake of Technology-Driven Solutions to Enhancing the Performance and Financial Management of Construction Projects
The performance and financial management of construction works, which is the primary requirement for successful project execution, has been significantly impacted by the information technology revolution, being increasingly intertwined with software solutions and technological advancements. Particularly, the financial management of construction works has undergone significant digitalization with specialized software packages for performing tasks like measurement and data management available. This study investigates the factors impacting the level of usage of the various software packages designed for the financial management of construction projects, as deployed in Port Harcourt. The study adopted a quantitative approach via the use of a structured questionnaire survey and data analysis. The study examined the broad spectrum of factors impacting software application in diverse project types, encompassing residential, commercial, and infrastructure developments, as well as varying project scales, ranging from small to large. The findings reveal that operational inhibitors are significant factors affecting software utilization. This insight suggests that challenges within operational processes, related to workflow integration or organizational dynamics, play a crucial role in shaping the extent of software adoption. This was followed by educational problems, in terms of the educational gaps or training needs within the industry. Additionally, the cost of software licenses is considered moderately impactful, highlighting the economic dimensions influencing quantity surveying-based software utilization. The study findings contribute valuable insights to academic discourse and industry practices, fostering technological adeptness in the local construction sector
Social Engineering and AI: Rights and Ethical Practices Fostering Sustainability
Artificial intelligence (AI) and social engineering combine to change organizational tactics, social norms, and human interactions. This convergence has the potential to be transformative in supporting sustainability in economic, environmental, and social dimensions. Social engineering itself is a tactic that manipulates human behavior and decision-making at its core, and artificial intelligence increases the power of this influence through predictive analytics, machine learning (ML), and the processing of large volumes of data. This fusion when being rooted within ethical practices while upholding for human rights can transform a powerful force for aiding SDGs, equity, and ecosystem preservation. This combination has the potential to be a game-changer for accomplishing the Sustainable Development Goals (SDGs), promoting equity, and protecting ecosystems if it is based on moral behaviour and in line with the protection of human rights. AI-powered social engineering can encourage responsible consumption, equitable access to resources, and creative solutions to global sustainability issues by tackling ethical issues like data privacy, transparency, accountability, and inclusion. The study promotes the responsible integration of social engineering and AI to build a future that places a high priority on social fairness, economic resilience, and environmental stewardship by encouraging interdisciplinary collaboration among politicians, technologists, and civil society. Using this perspective, the study emphasizes how crucial it is to incorporate ethical considerations into the creation and application of AI technologies in order to strike a balance between innovation and the values of sustainability and human dignity
Analysis of the factors affecting the Electric Vehicle (Two-wheeler) sales in India using Machine learning models
The adoption of electric two wheelers in India is the result of a complex interplay involving vehicle range charging infrastructure, technological advances, government policies and consumer perception. This study investigates these factors in detail to present a comprehensive understanding of how they influence the transition to electric mobility for environmental concerns and the need for reduction in dependence on fossil fuels. Electric two wheelers are emerging as a primary solution to reduce air pollution in cities while at the same time providing low-cost transportation. most of these barriers to wide scale adoption include range anxiety, inadequate charging infrastructure, high upfront costs and perceptual differences among consumers. Vehicle range is found to be the Most important factor affecting the purchasing decision of a consumer. Range anxiety or the fear of running out of power when one cannot locate a charging station remains a big stumbling block. Consumers maintained that long range electric two wheelers would dominate and made urgent calls for the development of advanced battery technology to help alleviate this anxiety. Availability and accessibility are relevant; their impact on buying decisions is lower compared to vehicle range. In other words that would imply mere expansion of charging infrastructure cannot give sufficient fillip to the adoption unless accompanied by substantial technological advancement in batteries. The results of this study point to the view that improvement in battery technology can help Original Equipment Manufacturers improve range and reduce the range concern for electric two wheelers. Demographic differences in the perception of vehicle range, charging station, quality reliability and the possible impact of new technologies like the battery swapping and fast charging solutions are some other avenues for future research. The study mentions the use of a decision tree model to analyze factors affecting consumer choices for electric vehicles. This model helps prioritize variables such as battery range and infrastructure development by splitting decisions hierarchically based on their importance. Additionally, future research could leverage models like predictive analytics and clustering algorithms to identify consumer behavior patterns, optimize charging infrastructure, and forecast EV adoption trends based on demographic and regional data Longitudinal studies may be able to track the trends of electric two-wheeler adoptions over a period and hence yield valuable insights on changes in consumer preference and long-term effectiveness in policy interventions. In sum accelerating the pace of adoption in India will need many prongs in its approach: technological advancement infrastructural development consumer education supportive policies all coming together for sustainable and greener transport of the future.
Impact of Technological Advancement on Nigeria’s Construction Industry: Examining the Compatibility of Quantity Surveying Functions
It is a widely acknowledged fact that technological advancements, particularly in the realm of information technology (IT), have brought about profound changes in the way professionals in the construction industry conduct business. These changes have led to cost reduction, labour efficiency, and time savings across various industries. The field of Quantity Surveying has not remained untouched by these transformations, as it too has adopted advanced technology in service delivery. This study examines the broader impact of technology on the construction industry, focusing on its current applications in Quantity Surveying. This study investigates the impact of technology, specifically examining how it influences the roles of Quantity Surveyors in managing cost, labour, and time. Deploying a survey research strategy, well-structured physical and digital questionnaires were administered to young and experienced Quantity Surveyors in Rivers State, Nigeria. This paper presents and analyzes the data collected through the questionnaires, as aligned with the research objectives, which were predominantly analyzed using descriptive statistics to elucidate the relationships between various study variables. The analysis outcome strongly supports the conclusion that technological innovation has significantly benefited the field of quantity surveying, influencing functions such as cost, time, and labour management in the Nigerian construction industry. Technological tools have collectively reshaped traditional quantity surveying practices in Nigeria, by introducing efficiency, accuracy, and a more data-driven approach to project management and decision-making. The integration of these technologies’ better positions Quantity Surveyors in Nigeria, to navigate the complexities of modern construction projects with enhanced capabilities.
The Evolution of Digital Payments in India: Assessing Impacts and Adoption Trends Before and After Demonetization
This study investigates the evolution of digital payment systems in India, analyzing the impact of demonetization on adoption trends and perceptions before and after the policy change. The research employs a mixed-method approach, utilizing a structured questionnaire to gather quantitative data from a stratified sample of Indian residents. The questionnaire explores demographic factors, frequency of digital payment usage, changes in cash usage post-demonetization, perceived security, satisfaction levels, and readiness for a cashless economy. Statistical analysis including Chi-Square tests, Wilcoxon Signed-Rank employed to analyze the data and draw insights into the factors influencing digital payment adoption. Qualitative insights from open-ended responses will complement quantitative findings. The study aims to provide a comprehensive understanding of India's cashless system, offering insights that can inform policies and strategies to promote further adoption of digital payments in the country. The result reveled that Demonetization didn't change the amount of cash in the economy much, and the economy grew at a similar rate before and after demonetization. However, people faced some immediate problems because of it. Also, after demonetization, more people started using digital payments instead of cash. the public faced a number of difficulties in the immediate wake of demonetization. Despite these difficulties, the use of digital payment systems increased noticeably after demonetization, suggesting a change in customer behaviour toward cashless purchases. This change indicates the possibility of a further integration of digital payment systems into regular economic activity in India.
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