International Journal of Engineering and Management Research
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Comparing the Effectiveness of Traditional vs AI-Based Recruitment Methods
Full text linkThe recruitment industry has witnessed a paradigm shift with the integration of Artificial Intelligence (AI) technologies, fundamentally reshaping how organizations approach talent acquisition. This research paper provides an in-depth comparative analysis between traditional recruitment practices—such as manual CV screening, face-to-face interviews, and human-led reference checks—and AI-enhanced recruitment solutions, including automated resume parsing, intelligent chatbots, video interview analytics, and predictive algorithms that forecast candidate success.
The study draws exclusively from secondary data sources, including industry whitepapers, academic journals, organizational case studies, and market trend reports, to assess how both methods perform across key dimensions: speed and efficiency, cost-effectiveness, objectivity and bias reduction, candidate engagement, and overall quality of hire. The paper explores how AI technologies streamline workflows, reduce human error, and enable data-driven hiring decisions, while also critically examining challenges such as algorithmic bias, lack of transparency, and the risk of depersonalization in the hiring experience.
By synthesizing current literature and real-world applications, the paper aims to compare traditional recruitment methods with AI-based recruitment techniques using secondary data sources such as industry reports, academic literature, and case studies. The comparison is structured around five critical dimensions: efficiency, cost-effectiveness, bias reduction, candidate experience, and quality of hire. Through this analysis, the study seeks to highlight the advantages and limitations of both approaches and offer strategic recommendations for organizations navigating the evolving recruitment landscape
Assessment of Skills Expected by the Employers in the Current Labor Market of Electrical and Electronics Technicians: A Case of Uasin Gishu County
Full text linkGlobally, unemployment of electrical and electronics graduates is one of the biggest challenges. This study aimed to assess skills that are expected by the employers in the current labor market of electrical and electronics technicians in Uasin Gishu County, Kenya. A mixed-method research approach and a descriptive survey research design were adopted. The Human Capital Theory guided the study. The target population comprised 165 electrical and electronics technicians and 11 electrical and electronics supervisors and their sample size was 55 and 11 respectively. Convenience sampling technique, total population sampling technique and simple random sampling technique were used to obtain the samples. The data collection instruments included questionnaire and interview schedule. A Cronbach\u27s alpha coefficient of 0.87 was attained for the pilot data. The quantitative data was analyzed using SPSS. The qualitative data was analyzed by presenting themes that emerged. Male electrical and electronics technicians, 33 (63.5%), were more than female participants, 19(36.5%). Most electrical and electronics technicians (50.0%) were aged between 20-24 years. Most electrical and electronics technicians 25(48.1%) were craft certificate holders. In respect to employment, most of the electrical and electronics technicians 9(17.3%) were employed by Kenya Power and Lighting Company. A large proportion of the respondents 30(57.69%) strongly agreed that supervisors require skills taught in technical institutions. Skills (F0.05 (1,52) = 5.030, p < 0.05), (b= 0.448, p< 0.05) significantly predicted employability. The study’s findings were stated and it was observed that there was a significant relationship between employability and skills. The study suggests that skills should be encouraged among the electrical and electronics technicians. The researcher recommends continuous learning and technical skill development through workshops incentives for pursuing advanced degrees that align with industry demands
A Survey Analysis of Students Spending Pattern of the Selected Colleges in Kolkata, West Bengal
Full text linkThis study presents a comprehensive account of expenditure behavior of students of some chosen colleges in Kolkata, West Bengal. Through the descriptive research method, primary level data were gathered using standardized questionnaires from 185 undergraduate students in relation to categories of expenditure, determinants, and demographic factors. The study reveals that huge numbers of students exhibit sensible monthly expenditure, spending below INR 1,000 on essential goods such as food, transport, and social excursions, portraying prudent economic behavior. High proportions of students maintain stable savings tendencies and are influenced by personal decisions and parental encouragement. Demographic factors, particularly income and source of funding, have a strong influence on attitudes towards spending, as revealed through statistical tests such as chi-square tests and measures of association like contingency coefficients. The study further identifies that spending frequency has a strong relationship with spending behavior, with shopping by month having a strong association. Further, the effect of extraneous factors like peer influence and media influence also varies across spending behaviors. The findings emphasize the demand for tailor-made financial literacy interventions to boost careful expenditures and savings amongst learners. Overall, such localized awareness may guide policy-makers, instructors, and monetary entities towards upholding financially accountable acts among scholars within Kolkata
Predictive Modelling for Customer Purchase Behaviour: A Logistic Regression Approach Based on Age and Estimated Salary
Full text linkCustomer purchase prediction has become a critical requirement in the insurance industry, where businesses strive to maximize customer acquisition while minimizing marketing costs. Accurate forecasting of whether a potential customer will purchase an insurance policy allows companies to focus on high potential leads and optimize their strategies. In this study, we propose a predictive modelling approach using logistic regression to classify customers based on two key demographic features: Age and Estimated Salary. A dataset of over 1,000 customer records was pre-processed, visualized, and divided into training and testing subsets using an 80:20 ratio. The logistic regression model was trained to identify significant patterns influencing purchase decisions and to estimate the probability of policy adoption. To enhance usability, the trained model was deployed in a Streamlit based web application that includes secure user authentication, interactive input fields, decision boundary visualization, and a leaderboard to track predictive outcomes. Experimental results demonstrate that the logistic regression model achieves an accuracy of approximately 90%, with strong interpretability through coefficient analysis and decision boundary visualization. This work highlights the potential of combining machine learning models with lightweight, interactive applications to support business analysts and decision-makers. The proposed framework offers a scalable, interpretable, and cost-effective solution for insurance companies seeking to strengthen customer targeting. Future work will focus on incorporating additional demographic and behavioral features, applying advanced ensemble models, and integrating large-scale realworld datasets to further enhance prediction performance
Innovation in Cybersecurity: Pioneering the Future of Digital Safety
Full text linkCybersecurity has surfaced as one of the most critical enterprises as the world becomes decreasingly connected. Digital pitfalls similar as hacking, ransomware, and data breaches are growing alarmingly, putting individualities, businesses, and governments at threat. Traditional cybersecurity styles no longer give sufficient protection. New technologies and strategies are being developed, leading to significant inventions in the field. This paper explores these inventions, fastening on technologies like Artificial Intelligence (AI), machine literacy (ML), blockchain, and Zero Trust security models. It also highlights the significance of collaboration between different sectors in driving these advancements. By assaying the current state and unborn directions of cybersecurity invention, this paper emphasizes the need for non-stop development and adaption to ensure the safety of digital means and systems
Case Study on the Current Status and Existing Issues of Japanese Language Courses for Minor Languages in Wenzhou City
Full text linkThe article takes schools in Wenzhou that offer Japanese language courses for the college entrance examination as an example. Based on the analysis of the current situation of Japanese language teaching in Wenzhou\u27s college entrance examination, the article uses a case study to analyze the existing problems in Wenzhou\u27s Japanese language teaching for the college entrance examination. It proposes suggestions such as arranging Japanese class time reasonably, improving Japanese language teaching teacher resources, addressing students\u27 own problems, improving teacher-student communication, guiding and encouraging students to learn independently, and enhancing students\u27 confidence in learning
A Literature Review on Integrating Enterprise Resource Planning and Supply Chain Management
Full text linkThe Integration of Enterprise Resource Planning (ERP) with Supply Chain Management (SCM) is the key to enhancing the effectiveness and efficiency of supply chains in today\u27s business environment. This paper explores the large body of research into the implementation of ERP systems within SCM and their related functions. It emphasizes the role that ERP plays in achieving operational efficiency and performance enhancement across supply chains. It indicates that the adoption of ERP solutions enhances business performance significantly, which includes better management of production processes, optimum management of inventories, and proper decision-making by enhancing data sharing. This research intends to conduct a systematic review of existing literature on ERP technologies in SCM, answering two primary sub-research questions: the implementation of ERP systems in SCM and the integration between these systems within supply chain processes. The research will be able to analyze numerous scholarly articles in order to identify trends, benefits, and challenges associated with ERP implementation in SCM. This will be invaluable insight for organizations contemplating the adoption of ERP, providing them with knowledge that will enhance their supply chain performance. Finally, this paper contributes to a deeper understanding of how ERP transforms SCM practices and drives competitiveness in an increasingly digitalized industrial landscape
Revisiting PPP Models for Climate Resilience and Disaster Risk Reduction in Indian Local Bodies: Challenges, Opportunities, and Financial Perspectives
Full text linkNatural hazards and climate change are frequently disrupting the communities. These effects are further amplified due to anthropogenic causes and unsustainable development. More often than not, due to limited capabilities, local government is the first victim of such disruptions. In this context, Public-private partnerships (PPP) models present a unique opportunity and overcome limited government funding limitations. This study attempts to explore the potential of PPP models in supporting and financing Climate Change Adaptation (CCA) and Disaster Risk Reduction (DRR) initiatives at the local-body levels. This study reviews both the international case studies from Jamaica, Japan and Latin America and national case studies from Delhi, India’s Smart City Mission. The study identifies the gaps in existing PPP frameworks and emphasises the need for innovative funding methods. The paper also discusses the importance of community engagement, multi-stakeholder engagement and traditional knowledge. Recommendations include enhancing financial resilience through blended finance, resilience bonds, and risk-sharing mechanisms. The paper proposes locally tailored PPP frameworks that prioritise long-term sustainability, capacity building, and responsive financing to foster climate-adaptive infrastructure
Mitigating Security Threats in IoT Networks Using Big Data Analytics and On-Device Modeling
Full text linkThe rapid increase of IoT devices created the modern digital infrastructures but it has also added crucial security challenges due to the scale and heterogeneity of IOT devices. This paper presents a collaborative security framework model that uses big-data analytics & Hybrid on-device modelling that allows us to address the security threats appearing in an IoT ecosystem. The framework uses big data analytics to process huge amounts of IoT traffic data in real time, recognizing patterns, detect possible threats and creates a miniature model that can be deployed on IOT device. On-device modeling helps to ensure that threats can be handled right there on-device, limiting cloud-based infra and latency dependencies. The framework also focus on using an AHD Model (Anomaly Hash-out Delta) using device behavior profile for permissible actions, which helps anomaly handling in a very lightweight machine learning model
Leveraging Digital Technology to Enhance Financial Inclusion through Microfinance in Tamil Nadu
Full text linkFinancial inclusion remains a critical challenge in developing regions, with many individuals lacking access to essential financial services. This study examines the potential of digital technology to enhance financial inclusion through microfinance in Tamil Nadu, India. The research investigates the impact of digital technology adoption on financial inclusion outcomes, considering the moderating effects of digital literacy and awareness among microfinance clients. The study employs a mixed-methods approach, combining quantitative surveys with qualitative interviews to gather comprehensive data from microfinance institutions and their clients in Tamil Nadu. The research utilizes a conceptual framework based on the Financial Inclusion Framework, incorporating digital technology as a key factor in improving access, usage, and quality of microfinance services. The study hypothesizes that digital technology adoption has a significant positive impact on financial inclusion through microfinance in Tamil Nadu, with digital literacy and awareness playing a moderating role. Data analysis will involve descriptive statistics, correlation analysis, and regression modeling to test the hypotheses and examine the relationships between variables. The findings are expected to provide valuable insights into the role of digital technology in enhancing financial inclusion and inform policymakers, microfinance institutions, and technology providers on effective strategies for leveraging digital solutions in the microfinance sector. This research contributes to the growing body of literature on digital financial services and financial inclusion, offering practical implications for improving access to financial services for underserved populations in Tamil Nadu and similar contexts