International Journal of Engineering and Management Research
Not a member yet
1311 research outputs found
Sort by
A Study on the Determinants of Organic Food Purchase Intention by Adult Consumers in Kolkata
Full text linkPurpose: The objective of the study is to evaluate how health, environment, awareness, affordability, taste and preferences influence Indian consumers’ intention to buy organic food.
Methodology / Design: A descriptive research design was employed. Data were collected through structured questionnaires distributed among urban consumers in India. The responses were analysed using descriptive statistics and regression analysis to examine the influence of key factors on purchase intentions.
Findings: Results indicate that health and taste considerations exert the strongest influence on buying decisions, while environmental and ethical concerns play a supporting role. Price and insufficient awareness remain major barriers to wider adoption.
Limitations: The study is limited to urban respondents and a moderate sample size, which may reduce its general applicability across rural populations.
Practical Implications: The findings provide guidance for marketers and policymakers to highlight health and taste benefits in promotional strategies, while simultaneously working to address cost and knowledge-related barriers.
Value / Originality: This research enriches the limited body of work on organic consumer behaviour in India by offering empirical evidence from an emerging market, thereby contributing to a better understanding of how demographic and attitudinal factors shape organic food adoption
IntelliLearn: Empowering Education Through Artificial Intelligence: A Comprehensive Indian Perspective
Full text linkGlobal education ecosystems are being rapidly transformed by artificial intelligence (AI), and India is no exception. India, which has a population of over 1.4 billion and one of the most intricate educational systems in the world, stands to gain a great deal from the thoughtful application of AI in administration, education, and learning. This essay summarizes recent developments and empirical studies on artificial intelligence (AI) in Indian education, focusing on curriculum development, inclusive learning, virtual classrooms, administrative effectiveness, and individualized learning. The paper examines how AI-powered technologies such as adaptive platforms, speech recognition, AI tutors, and real-time analytics are changing Indian classrooms using national statistics, recent case studies, and international literature. In addition, the study identifies the infrastructure and ethical issues that are specific to India and provides practical policy suggestions that complement NITI Aayog\u27s AI strategy and NEP 2020. For researchers, educators, and policymakers, visual data provides strong insights that bolster important conclusions
GenAI Based YouTube Video Summarizer
Full text linkThis paper proposes an intelligent, web-based application—AI video summarizer—that efficiently extracts, Tran- scribes, and summarizes YouTube video content using advanced AI models such as Google Gemini. By simply entering a video link, users can obtain multilingual transcripts (in English, Hindi, and Marathi), concise summaries, and time stamped highlights of key moments. Furthermore, the application converts the generated summaries into audio using GTTS and offers options to download or copy full transcripts. Built with Streamlit, it provides an interactive and user-friendly interface. This solution addresses the growing challenge of overwhelming digital video content, offering a more accessible, time-saving, and language- inclusive way to understand and utilize video information across various fields
A Study of the Performance of the Non-Performing Assets of the Rural Co-Operative Bank of India
Full text linkThe study of Non-Performing Assets (NPAs) in Rural Co-operative Banks of India focuses on understanding how these bad loans affect the financial health and performance of these banks. Rural cooperative banks play a crucial role in providing credit to the agricultural and rural sectors, supporting millions of members and contributing significantly to rural development. However, the rising level of NPAs poses a serious challenge, as it reduces profitability, erodes the asset base, and threatens the sustainability of these banks. This research aims to analyze the trends and causes of NPAs in rural cooperative banks, assess their impact on profitability and productivity, and suggest measures to manage and reduce NPAs effectively. The study highlights that factor such as priority sector lending, government debt waivers, and credit risk in rural lending contribute to the growth of NPAs. Addressing these issues is vital for strengthening the cooperative banking sector and ensuring its continued support to rural economies
Estimation of Delay in Prefabricated Projects Using Modern Machine Learning Approaches (Case Study: Baghdad)
Full text linkWe analyzed the prior literature on estimating delays in project time in the presentation section, and we found that data uncertainty could be minimized using generative adversarial network (GAN) to augment our dataset, which uses data to produce findings that mimicked actual world circumstances. We organized the findings accordingly. In the initial finding, we utilized four (4) algorithms on a dataset of twenty-one features containing 284,807 transactions i.e. multilayer perceptron (MLP) neural network, support vector machine (SVM), decision tree, and k-nearest neighbor (KNN). The findings established that MLP neural network produced the largest accuracy value of (90.72%), followed with SVM (78.43%), Decision Tree (77.64%), and KNN (74.5%).
Next, the GAN was used to augment the dataset to a total of 400,00 transactions, allowing the augmented dataset to result in a number of delay samples of 609. The four (4) algorithms were subsequently re-evaluated with the expanded dataset to classify and identify project delays in the dataset. The results indicated that augmentation using GAN enhanced the accuracy of the models overall. From the first process, using the MLP neural network reached an accuracy of 98.76% and SVM was 82.03%, decision tree was 80.31% and KNN was 79.95%
A Study on the Role of Fintech Adoption on Employee Engagement in NBFCs: Challenges and Opportunities
Full text linkIn the Indian financial services industry, fintech has become a disruptive force, with non-banking financial companies depending more and more on digital technologies to improve productivity, reach, and competitiveness. This study looks at how fintech adoption affects employee engagement in Indian NBFCs, focusing on adoption trends, implementation difficulties, and engagement-related prospects. The study, which employs a conceptual and descriptive research approach, is founded on a methodical thematic examination of pertinent secondary literature on employee engagement, fintech, and digital transformation. The results show that the adoption of fintech alters work procedures and employee responsibilities, which in turn affects engagement through modifications to job descriptions, skill requirements, and work experiences. While fintech provides chances for job enrichment, learning, and enhanced performance, it also introduces new issues such as uneven digital preparation, technostress, and aversion to change. The study found that fintech improves employee engagement when combined with excellent training, communication, and human-centered change management. The report concludes by underlining the need of NBFCs aligning technological initiatives with staff development, as well as proposing further empirical research to investigate employee-centric outcomes of fintech adoption across varied organizational contexts
DEX – Digital Employee Experience at Digital Workplace; Challenges and Strategic Implications for Organization Practicality
No full textDigital Employee Experience (DEX) refers to the experience of employees while interacting with the workplace technology, digital tools, information technology systems and infrastructure. The experience is not only focusing the technology infrastructure and applications, but also mainly focusing on the intuitiveness, supportiveness and efficiency of the technology tools in helping the employees performing everyday duties and tasks. The study plans to discuss the employee experience in the digital workplace and its importance for efficient organization functioning and to discuss the practicality of the organizations in challenges. There are a few studies has dealt with DEX in a qualitative way of discussion. This study applied qualitative approach using literature survey method to collect the existing literatures related to DEX and sustainable digital workplace for sustainability of the organizations. The study adopted qualitative literature review content analysis using summative method for discussing and reporting. The study explored the digital workplace tools for Collaboration and communication – Slack, Google Hangouts, Face book in workplace, for Project Management – Base camp, Asana, Trello, Write, for Remote Desktop – AnyDesk, Chrome Remote Desktop, Remote PC, for Time Management –Toggl, Clockify, Harvest, for Screen Sharing and recording –Team Viewer, Screen leap, Join .me, for Video Conferencing & Tele working – Skype, Zoom, Cisco Webex, and for Cloud Storage - Google Drive, Google Docs, Dropbox, One Drive. The study identified challenges like integrating business needs with business operations and functions, techno stress among employees, competence needs, security issues, Tele – working technological issues and lack of end user experience among employees. Further it discussed the implications for DEX are Setting the infra-structure, Tele-working necessary model equipment, Creating Positive DEX by streamlining digital tools and updates, Provision of end user experience to employees and competency needs among employees
A Study on the Impact of Snapchat Usage Among Youth in Navi Mumbai: Trends, Influences, and Social Behaviour
No full textSnapchat, a leading platform in ephemeral messaging, has significantly transformed the digital habits of young people around the world. In urban areas like Navi Mumbai, it has evolved beyond being just another social media app—emerging as a powerful tool for self-expression, peer bonding, entertainment, and identity construction. This study explores how youth in Navi Mumbai engage with Snapchat and examines the broader social, psychological, and emotional implications of its use. Using a mixed-method approach through surveys and interviews, the research identifies motivations such as privacy concerns, the desire for authenticity, entertainment needs, and peer pressure. Furthermore, it explores the impact of Snapchat on self-image, social interactions, attention span, and emotional well-being. The research highlights key concerns, including digital dependency, reduced face-to-face communication, and the pursuit of online validation. Practical recommendations are offered for educators, parents, and policymakers to promote healthier, more balanced digital practices among youth
Evaluating the Effectiveness of 360-Degree Feedback (EKSUTRA INDEX)
No full textPerformance appraisal is a critical function in workforce management, influencing employee motivation, career development, and organizational efficiency. Traditional performance evaluation systems often suffer from subjectivity, inconsistencies, and biases, limiting their reliability and effectiveness. This study introduces the Ekasutra Index, a structured, data-driven, and statistically validated model for performance appraisal that leverages 360-degree feedback mechanisms. Unlike conventional methods that rely primarily on managerial assessments, the Ekasutra Index integrates feedback from supervisors, peers, subordinates, and self-assessments to derive a comprehensive performance score. The study employs a five-point Likert scale and a Weighted Average Mean approach to consolidate multi-source feedback into a single, quantifiable score. Furthermore, Partial Least Squares Structural Equation Modeling (PLS-SEM) is utilized to assess the impact of different evaluation components on the final performance index. The reliability of the model is validated through Cronbach’s Alpha (0.91), ensuring high internal consistency. Additionally, Gaussian Copula (GC) adjustments are applied to account for potential endogeneity issues, confirming the robustness of the assessment framework. Key findings indicate that self-assessment (β = 0.430) and supervisor evaluation (β = 0.394) exert the strongest influence on the Ekasutra Performance Index (EPI), while peer review (β = 0.200) and subordinate feedback (β = 0.363) have relatively lower but still significant effects. The automated and statistically validated nature of the model enhances transparency, eliminates subjectivity, and improves decision-making efficiency for HR professionals. The Ekasutra Index presents a scalable and adaptable framework that can be implemented across corporate organizations, educational institutions, and public sector agencies. By integrating data science principles with structured employee feedback, the model fosters fairness, transparency, and efficiency in performance evaluations. This study contributes to the growing field of data-driven human resource management, providing a robust methodology for organizations seeking to optimize their appraisal systems in the era of digital transformation
Forensic Asset Tracing Efficacy on Fraud Detection of Nigerian Listed Firms
Full text linkThis study examines the efficacy of forensic asset tracing in detecting fraud in Nigerian listed firms. With rising corporate scandals, forensic accounting has become a crucial tool for fraud prevention and detection. This research employs a mixed-method approach, combining qualitative and quantitative data to assess forensic asset tracing\u27s impact on fraud detection. Primary data were collected through structured questionnaires distributed to forensic accountants, auditors, and regulatory officers, while secondary data were sourced from financial reports and fraud cases. The study employs regression analysis to evaluate the effectiveness of forensic asset tracing mechanisms in curbing fraudulent activities. Findings reveal that forensic asset tracing significantly enhances fraud detection, particularly when integrated with robust regulatory frameworks and corporate governance practices. The study identifies key challenges, including regulatory bottlenecks, limited forensic expertise, and inadequate technological adoption, which hinder forensic asset tracing\u27s full potential. The research contributes to literature by providing empirical evidence on the role of forensic accounting in fraud detection in Nigeria. It recommends strengthening forensic accounting practices through capacity building, regulatory enhancements, and the integration of advanced digital forensic tools. The study concludes that forensic asset tracing is an effective tool for fraud detection but requires a supportive regulatory environment and skilled professionals. Future research should explore the role of artificial intelligence in forensic asset tracing and fraud detection