The rapid advancement of digital technologies has transformed organizational learning and development practices across industries. Online training and e-learning platforms have emerged as flexible, costeffective, and scalable solutions for enhancing employee competencies in dynamic business environments. In this context, organizations are increasingly adopting digital learning systems to improve skill development, knowledge retention, and overall job performance. The research identified the effectiveness of online training programs and e-learning platforms as well as it also studied the role of online training in enhancing employee skills and job performance. The research was conducted in the Hyderabad city of Telangana State. As the city is the home of so many industries so the four prominent industries were selected i.e. IT, Banking and Finance, Retail and Education. By using the purposive sampling method a sample of 328 professionals was selected. The data was collected by using Google docs form and same was analyzed by using SPSS 25.0. The data analysis revealed that majority of

India's air quality crisis is severely impacting the country's resource management, posing a challenge closely linked to rapid urbanisation, demographic growth, and energy poverty. The most critical issue that is hardly ever mentioned is that of Indoor Air Quality (IAQ), where household air pollution (HAP) from the use of solid fuels (e.g., biomass) is responsible for the release of pollutants that lead to about 600,000 premature deaths per year, in general, in the countryside and among lowincome communities. The research proposes a cross-sectoral solution scheme that would replace the reactive, city-centred approaches with systemic, airshed-level governance. Among the main measures are: Clean Fuel Transition and provision of targeted subsidies for LPG refill; carrying out Extended Producer Responsibility (EPR) to change waste (including plastics) handling accountability from consumers to producers; and changing the face of the city by means of

This study examines the influence of behavioral biases on individual investor's decision-making tendency in the context of the National Stock Exchange (NSE) of India. It examines four important biases which are overconfidence, herding, anchoring, and the disposition effect. The analysis chapter utilizes Descriptive Statistics and Partial Least Squares Structural Equation Modeling (PLS-SEM) for data analysis, drawing on survey data from 243 respondents from different regions of the country. The findings of the study divulge that overconfidence bias and regional financial instability significantly impacts investor decision making tendency, whereas herding behavior bias plays an important role in influencing the information search process. Notably, the disposition effect bias has typically considered as a negative bias, reveal a positive correlation with decision-making behavior in this context.

This article presents the organizational and legal aspects of the internal control system for personal data processing amid the transformation of telecommunications companies into multifunctional digital ecosystems. The full life cycle of personal data is analyzed, and a classification by degree of criticality and risk is proposed. To effectively prevent threats, the necessity of implementing a risk-oriented "Three Lines of Defense" model is substantiated. The authors systematize the regulatory and legal framework governing personal data processing in Russia, with an emphasis on industry-specific legislation and regulatory requirements. Acomparative analysis of federal legislation and the European GDPR is also conducted, identifying the applied challenges of forming a hybrid compliance framework. In assessing the market environment, it is established that the Russian telecommunications market constitutes an oligopoly with a high concentration around the "big four" companies. Based on data from the four largest telecom operators, an assessment is made of the maturity of control procedures at all stages of personal data processing. The findings of this study can be used to improve the effectiveness of risk management systems in the telecommunications industry and to adapt compliance procedures to the current challenges of

Background: Retail investors in India often invest their personal savings into the stock markets using traditional, full-service brokerages, discount brokers, and online brokers to manage their assets, make investments for their gain, and execute trades. These investors are not trained professionals who look to enhance personal earnings through their investments. Objectives: The paper aims to examine the impact of Robo Advisory platform services in investment management leading to customer satisfaction using modern portfolio theory. Methodology: The primary responses were collected from 384 Investors residing in Bangalore, Chennai & Hyderabad using the Cluster Sampling method through Survey Questionnaire. Aesthetics, security, ease of use, user experience, and e-satisfaction are the broader variables considered for the study. JAMOVI & JASP software was used to examine the hypothesis through Simple Percentage Analysis, Structural Equation Modelling and Machine Learning.

Purpose: The study aims to explore how HRM 4.0 technologies can be applied in talent acquisition and management systems using the Interpretive Structural Modelling (ISM) approach. It focuses on identifying the key factors and their relationships in shaping a modern HR system. Design/Methodology/Approach:The research applies the ISM method to analyse the interactions among selected HRM 4.0 elements such as artificial intelligence, data-driven decision-making, automation, and digital tools. Expert opinions and literature review are used to build a structural model. Findings:The results highlight that digital integration, AI, and real-time analytics are major drivers of HRM 4.0 in talent acquisition. These factors improve recruitment accuracy, employee engagement, and decision-making efficiency. Research Limitations:The study is limited to selected factors and expert inputs, so the results may not represent all industries or regions. Practical Implications : Organizations can use the findings to design better HR strategies, adopt suitable technologies, and improve workforce planning and retention.

Women's empowerment at the local level requires sustained and equitable financial support, particularly through effective fiscal management and inclusive public investment. With the accelerating rate of digital transformation, digital financial services and governance approaches appear to have a key role to play in promoting ?scal transparency, better resource mobilization and allocation, and the provision of finance opportunities for the empowerment of women at all levels. These are opportunities that can be maximized through the effective use of ?scal rules and the potential afforded by digital transformation, and sustainable financing frameworks to foster local women's economic and social empowerment. The present study examines the role of fiscal rules, digitalization, and sustainable financing in promoting women's empowerment at the local level. A mixed descriptive and causal research design was adopted to assess the existing status of these factors and analyze their influence on women's empowerment. The study was conducted among adult women in Noida City, and a sample of 429 respondents was selected using purposive sampling. Primary data were collected through a structured questionnaire comprising sections on demographic profile, nature of fiscal rules towards women's empowerment, digitalization status among women, women's access to sustainable financing, status of women's empowerment, and challenges in accessing digital financial services and sustainable finance.

The article studies the features of marketing and logistics activities of enterprises. The influence of external factors on logistics activity within Ukraine is substantiated. The state of the transport and logistics industry in Ukraine in recent years has been analyzed, which made it possible to determine the relevant trends and cause-and-effect relationships. The regional features of the territorial location of logistics enterprises within Ukraine have been studied, which has made it possible to form directions for optimizing their activities depending on the potential of the territory. The directions of logistics activity in Ukraine have been investigated with the allocation of the period of pre-war state, martial law and postwar recovery, which made it possible to single out the features of each of the periods. A methodological approach has been proposed, which has provided a comprehensive and objective assessment of the marketing and logistics activities of leading logistics and postal enterprises in the dynamic conditions of digitalization of the business landscape of the regions. The proposed logic of the study is consistent, provides methodological consistency of the study and made it possible to move from the analysis of individual indicators to the formation of strategic conclusions on the optimization of marketing and logistics activities of enterprises in the context of digitalization of the business landscape of the region. The analysis of marketing and logistics indicators of the activities of leading transport and logistics enterprises is carried out. A

Predicting dynamic behavior in complex organizational-technical systems such as industrial ecosystems, energy grids, and transportation networks faces substantial challenges. These include pronounced nonlinearity, inherent uncertainty, and acute data scarcity. Traditional machine learning methods deliver high predictive accuracy but remain largely uninterpretable, whereas ontological models provide semantic clarity yet suffer from static representations. A hybrid synergy proves necessary, one that effectively bridges inductive learning with deductive logical inference. This study proposes a hybrid approach for predictive modeling and scenario analysis, wherein an ontological model functions as an active semantic constraint for ensemble methods resilient to smallsample settings. Our architecture incorporates a three-tier semantic filter: an ontological layer formalizing system structure, axioms, and causal rules; a predictive layer that leverages an ensemble comprising Bayesian Ridge regression, Gaussian Process Regression, and Elastic Net and a scenario layer designed to generate alternative trajectories.

This study investigates the impact of electronic banking (e-banking) service quality on customer satisfaction in the Commercial Bank of Ethiopia (CBE) operating in Nekemte Town. The study aims to identify key service dimensions influencing customer satisfaction and provide managerial insights to improve digital banking performance. A descriptive and cross-sectional survey research design was employed. Primary data were collected from 348 active customers of CBE using a structured questionnaire based on a five-point Likert scale. Quantitative analysis was conducted using Pearson's correlation and multiple linear regression with SPSS version 25 to assess the relationship between five e-banking service quality dimensions—reliability, ease of use, delivery time, security, and overall service quality—and customer satisfaction. The results revealed a strong positive and statistically significant relationship between e-banking service quality and customer satisfaction (R² = 0.758). Among the predictors, service security and delivery time showed the highest influence on customer satisfaction, followed by reliability, ease of use, and service quality. The findings suggest that CBE should prioritize enhancing system security, improving transaction speed, and strengthening reliability to increase customer satisfaction and sustain competitive advantage in Ethiopia's digital banking sector. This study extends the SERVQUAL framework to the context of e-banking in Ethiopia, providing empirical evidence from a developing-country perspective and offering practical recommendations

The rapid advancement of artificial intelligence (AI) has significantly transformed human resource management practices, particularly in the recruitment and selection process. Organizations are increasingly adopting AI-based recruitment tools to streamline hiring procedures, reduce time and cost, and improve decision-making efficiency. In addition, these technologies are reshaping candidates' experiences by enabling faster communication, personalized interactions, and transparent recruitment processes. The research was aimed to identify the role of AI based recruitment tools in enhancing hiring efficiency and candidate experience. The research was done in New Delhi and all the companies using AI driven recruitment tools were considered as population of study. By using the purposive sampling method, a sample of 127 HR professionals was included in the study. The respondents were contacted personally and data was collected through survey method with the help of enumerators. The data was analyzed by using one sample t-test and chi-square test. The results revealed that majority of HR professionals were familiar with AI based recruitment tools and they generally used these tools for candidates sourcing, screening and communicating with candidates. The age and experience of HR professionals have significant impact on their awareness about AI based recruitment tools. The hypothesis testing concluded the significant impact of AI based recruitment tools in enhancing hiring efficiency and candidate experience. Overall HR professionals were satisfied with the performance of AI based recruitment tools. Key Words: AI based recruitment tools, Hiring efficiency, Applicant tracking system, AI assessment tools

The ongoing war in Ukraine has resulted in the destruction of infrastructure valued at USD 486 billion. Most of the documented damage is in ten specific oblasts. Although reconstruction of these oblasts is a high priority, the analysis of regional reconstruction studies focusing on the economic and investment aspects of reconstruction has been virtually non-existent. The goal of this research is to establish the framework for the quantitative analysis of the reconstruction of these ten oblasts using a combination of methods. This research will focus on the construction of a sub-component Composite Investment Attractiveness Index (IAI) as a key component. The analysis will be supplemented by the assessment of the Net Present Value (NPV) and the Internal Rate of Return (IRR) with three reconstruction cost estimates of USD 175.1 billion (Pessimistic, Baseline, and Optimistic). The declared reconstruction estimates show a range of total NPVof USD - 42.2 billion (Pessimistic) to USD - 9.8 billion (Baseline), and USD +97.0 billion (Optimistic). The Baseline breakeven discount rate is 6.95%. Zonal differentiation is highly evident.