1. Key Components of Employee Engagement: An Observational Analysis of MSME’s in Kashmir
Authors:-Dr. Khanday Sadaf un Nisa, Dr. Altaf Hussain Naik
Abstract- Purpose- Employee engagement is a topic of concern for every enterprise be it small or medium. Success of enterprise is determined by its capability of retaining best talent. The purpose of this paper is to determine the factors that influence employee engagement. If the factors that have positive impact on employee engagement are known, then it becomes easy for the enterprise to act accordingly on those factors. Methodology- Data was collected from a sample of 68 employees from different small and medium scale enterprises operating in various districts of Kashmir. Findings- Financial factors mostly influence employee engagement. If employees are provided with impartial compensation and rewards for their work, they feel motivated and give their best. Enterprises that take into account career development opportunities for employees succeed in improving employee engagement. Enterprises should work on all the factors like financial, growth, motivation in order to compete globally because employee engagement now a days has gained global importance. Research limitations- The study involves a small sample of only 68 employees. Employee preferences vary, some factors may influence an employee in one way and same factors may influence the other employee differently. So further research can be conducted on this study. Practical implications- The study highlights the significant strategies that enterprises can use in order to increase their employee engagement. The study is helpful for the enterprises who want to retain talent and improve engagement levels of employees. Value/ originality- The paper is contributing to the existing body of knowledge related to employee engagement. Previous literature has lacked in determining employee engagement with respect to small and medium scale enterprises of Kashmir.
2. The Impact of Government Sustainability Policies on Consumer Behavior: A Comprehensive Literature Review
Authors:-Assistant Professor Meenakshi Singh, Associate Professor Dr Meenakshi Duggal, Assistant Professor Dr. Poonam Singh
Abstract- This literature review explores the dynamic relationship between government sustainability policies and consumer behavior, aiming to unravel the profound impact of policy decisions on the choices that shape our daily lives. The analysis reveals that government sustainability policies have been found to be effective in promoting sustainable practices and influencing consumer behavior. Studies have shown that energy efficiency policies significantly impact consumer choices, leading to increased adoption of energy-efficient appliances and more fuel-efficient cars. Government policies in the realm of sustainable transportation have also shown significant influence on consumer behavior, promoting the adoption of electric vehicles. Waste management and sustainable food policies have garnered attention as well, shaping consumer attitudes and behaviors towards recycling, waste reduction, organic farming, and sustainable agricultural practices. The analysis further highlights the importance of responsible and strategic green marketing approaches in meeting consumer demand for environmentally responsible products. Factors such as individual attitudes, social influence, and various contextual factors play significant roles in shaping consumer behavior towards sustainability. Bridging the intention-behavior gap in ethical consumerism and addressing the challenges associated with green marketing require a comprehensive understanding of the factors that influence consumer behavior. This literature review provides valuable insights that can inform the development of effective strategies and policies to promote sustainable consumption behavior and pave the way for a more sustainable future.
3. Key Factors Influencing the Implementation and Adoption of the Electronic Mastercard Register at Mlangeni Health Centre
Authors:-Daniel Devoted Matemba
Abstract- The study utilized a mixed-methods research design, combining quantitative surveys and qualitative interviews, to examine the factors influencing the successful implementation and adoption of the EMR. The results revealed a strong relationship between specific factors and the perception of the EMR doing good, which is crucial for successful adoption. Key findings include the significant negative impact of inadequate training and support, concerns about data security and privacy, and the absence of evidence regarding the benefits and value of the EMR. On the other hand, factors like resource availability, user-friendliness, and regulatory and legal considerations were not found to be statistically significant in influencing the perception. These results provide valuable insights for healthcare policymakers, administrators, and other stakeholders. By addressing the identified barriers and leveraging the enablers, healthcare facilities in similar settings can better plan for the successful integration of technology, ultimately improving operational efficiency and patient care. This study addresses a critical problem in the healthcare sector, namely, the lack of a comprehensive investigation into the specific factors influencing the adoption of EMR systems in resource-constrained environments. Its findings contribute to bridging this research gap and offer practical recommendations for enhancing technology adoption, thereby positively impacting healthcare services in Ntcheu District and beyond.
4. Structural Equation Model for Assessing Goodness of Fit on CSR Impact Factors
Authors:-Assistant Professor Dr.D.Dharmadhurai, Assistant Professor Dr.D.Shakila
Abstract- This study focuses on IT companies engaging in various CSR activities, with CSR standing for Corporate Social Responsibility. In India, the economy has experienced significant growth over the past two decades, largely due to the expansion of the Information Technology (IT) and Information Technology Enabled Services (ITES) sectors. Corporate citizenship and CSR are closely tied to corporate behavior and reputation. When companies are socially responsible, they can effectively address societal concerns and demonstrate their impact. Different IT companies have adopted various CSR initiatives, and in India, CSR is mandated by Section 135(1) of the Companies Act, 2013, with indicative areas of CSR listed under Schedule VII. The objectives of this study are twofold: (a) to assess the influence of CSR practices adopted by IT companies on societal development and the benefits to the company, and (b) to build a structural equation model (SEM) for goodness-of-fit and test whether any statistical significance exists regarding CSR impact factors. AMOS (Analysis of moment Structures), a visual program for SEM, was used for this study. SEM was employed to test a model based on observed data from CSR-practicing IT companies in Chennai City, India. AMOS calculated a modification index for all constrained parameters in the model, indicating how much the chi-square value of the model would decrease if a parameter were free instead of constrained. Using these modification indices, the model was refined to achieve a better fit. The software packages used for data analysis and testing in this study were IBM SPSS and AMOS.
5. A Study on Impact of FDI on Indian Economy– An Empirical Analysis
Authors:-Assistant Professor Dr. Pooja Kumari, Associate Professor Dr. R. Vennila
Abstract- A lot of debate is going on about the importance of foreign direct investment (FDI) in the process of growth has been hot in a number of nations, including India. The foundation and prerequisite for economic development and growth is investment. In addition to a country’s foreign exchange reserves, other factors that are essential to its health include exports, government revenue, financial status, the amount of available domestic savings, and the volume and caliber of foreign investment. The aim of this study is to analyze the impact of FDI on Indian Economy. To meet the objective of the study time series data is used 2005 to 2023. Variables used in the study are Foreign Direct Investment as dependent variable and Gross Domestic Capital Formation (GDCF), export, import, Gross Domestic Product (GDP), Foreign Exchange Reserve (FER) and Wholesale Price Index (WPI)as independent variables. Techniques used in this study are descriptive test, correlation and regression analysis. FDI exhibits a positive correlation with variables such as GDCF (0.44), Export (0.38), Import (0.42), GDP (0.44), and FER (0.44). This implies that a 1% increase in FDI corresponds to a corresponding degree of change in other variables. The correlation between WPI and FDI is negative, or – 0.22, indicating that the two variables go in different directions. The study concluded that FDI statically significantly impact on Indian economy.
Impact of Artificial Intelligence and Machine Learning on Business Operations
Authors: Assistant Professor Gangawane Anand Bhagwat
Abstract: We live in a digital age when artificial intelligence (AI) and machine learning (ML) are changing the face of business. When organizations use a vast array of data to run their business — from customer interactions to sensor signals — they can automate things, see actionable patterns, and improve their decision making. But many companies fail to scale their pilot projects into companywide benefits, crippled by data silos, skills gaps and ethical concerns. Objectives: This study explores the extent and use of AI/ML for the critical business value drivers, including process automation, decision support, customer engagement, supply-chain availability, and human resources management. Its objectives are to assess efficiency and accuracy gains of the solution and to understand organizational drivers and barriers, and to suggest a human-centred framework for responsible AI/ML adoption. Methods: We conducted a systematic literature review that included peer-reviewed articles, conference papers, and industry reports published from 2014 to 2022. Searches on “AI business operations” and “machine learning adoption” were conducted in Scopus, Web of Science, and Google Scholar. Two authors screened more than 1200 records independently and used the inclusion criteria to identify 75 highly relevant articles. Data extraction used a template tested with a pilot of studies and recorded intervention types, outcomes, and implementation questions. Qualitative metrics and narrative insights were reduced using thematic analysis. Results: All AI/ML implementations delivered double-digit enhancements: cycle times were reduced up to 60 60%, forecasting errors were reduced by 50%, cross-sell rates increased by 25%, stockout frequency dropped by 67%, while time-to-hire fell by 70%. Case studies in manufacturing, retail, finance, and telecom showed that the key to success manifests through solid data pipelines, tight stakeholder involvement, and a strong human-machine collaboration. Conclusion: AI/ML value will not be realized if implementations cannot be treated as socio-technical change. Businesses that marry technical proficiency with ethical stewardship, clear performance measurements, and ongoing employee upskilling will be most adept at leveraging AI/ML as strategic disruptors for agility, efficiency innovation.
Distributed Log Correlation And Audit Readiness In NIH Unix Environments
Authors: Naveen Raj,, Lavanya Priya, Sindhuja V, Jeevan S.
Abstract: Distributed log correlation has become a cornerstone of operational integrity and audit preparedness in large-scale scientific computing environments like those found at the National Institutes of Health (NIH). Within NIH's UNIX-based infrastructure which spans Solaris, AIX, and Red Hat Enterprise Linux (RHEL) ensuring a unified view of disparate log streams is essential to maintain data integrity, respond to security incidents, and satisfy regulatory mandates such as FISMA, HIPAA, and NIST SP 800-53. The sheer heterogeneity and volume of system logs present a significant challenge for IT administrators aiming to implement a consistent, scalable, and audit-ready logging infrastructure. This review explores how distributed log correlation, centralized aggregation, and normalization pipelines are employed to overcome these challenges. Key tools including syslog-ng, rsyslog, auditd, Splunk, and ELK Stack serve as the foundation for ingesting, transforming, and analyzing logs from various platforms and services. These tools are supplemented by custom shell and Python scripts for ETL (Extract, Transform, Load) processes and correlation enrichment. Real-time correlation engines, timestamp normalization, and structured alerting mechanisms allow NIH IT teams to rapidly detect anomalies and initiate automated triage, supporting both operational visibility and forensic traceability. The article further examines retention policies, immutable logging practices, and metadata enrichment strategies that help establish reliable audit trails. Through real-world examples from NIH data centers, including rogue job detection in HPC clusters and login anomaly analysis on air-gapped Solaris nodes, we illustrate the practical outcomes of implementing such a framework. Finally, we explore future trends such as machine learning-based anomaly detection, cloud integration for hybrid research environments, and compliance-as-code techniques. These strategies collectively support NIH’s mission of secure, compliant, and data-resilient biomedical research infrastructure
DOI: https://doi.org/10.5281/zenodo.16152688
