In the rapidly evolving landscape of online Retail, integrating Virtual Reality and Augmented Reality technologies has transformed the shift from traditional e-commerce to immersive v-commerce, particularly gaining speed in the COVID-19 pandemic. The study evaluates consumer awareness of VR try-on features, assesses shopping experiences, and identifies perceived benefits and challenges. .it also indicates that Augmented Reality (AR) and Virtual Reality (VR) significantly enhance user engagement and satisfaction by allowing consumers to visualize clothes in a real-time manner, which helps to reduce return rates and increase purchase confidence. This study provides insights for retailers on leveraging immersive technologies to enhance customer experiences and increase sales, which shows the importance of understanding consumer perceptions in the evolving digital marketplace.

Amidst the algorithmically accelerated saturation of digital marketplaces, influencer-mediated persuasion has become a dominant force in shaping consumer psychology and market dynamics. This study offers a theoretically grounded and empirically validated exploration of how influencer credibility, content consistency, and relatability function as antecedents to consumer trust, which subsequently catalyses engagement and drives purchase intention. Utilizing Structural Equation Modelling on data gathered from 300 active users across Instagram, YouTube, and Snapchat, the research constructs a multi-stage behavioural framework that foregrounds trust and engagement as sequential mediators. Findings reveal that while influencer credibility exerts the most significant effect on trust, the translation of trust into engagement—and subsequently, into purchasing behavior—is profoundly moderated by platform dynamics. Specifically, long-form content platforms such as YouTube amplify the trust-engagement nexus more effectively than ephemeral short-form environments like Snapchat, suggesting that narrative depth and perceived authenticity intensify persuasive outcomes. The study enriches existing literature by integrating parasocial interaction theory and source credibility frameworks into a cohesive trust-engagement-purchase intention model, offering a robust lens for understanding influencer marketing efficacy. Practically, it directs marketers toward crafting platform-contingent strategies that privilege authentic interaction and longitudinal credibility over transient reach or superficial aesthetics. As digital influence evolves into a complex socio-commercial phenomenon, this work underscores the imperative of aligning content form, influencer behavior, and audience psychology to optimize consumer engagement and behavioural conversion.

This research is focused on the possibilities of Artificial Intelligence (AI) and analytics to bring innovation to Human Resources (HR) and sustainable financial growth in an organization. Based on the theories of Resource-Based Views, Human Capital Theory and Sustainable Growth Theory, the study investigates how AI-informed HR analytics support talent acquisition, employee engagement, performance management and workforce planning. The results have shown that inclusion of AI in the recruitment processes enhances the efficiency of the recruitment by 35%, its workforce improvement by 18% and a reduction in the cost of operation of the HR department by 22%. The long-term profitability is enhanced through these improvements in that they aligned the HR strategies with long organizational priorities, maximized on the use of resources, and developed the culture of the continuous improvement. Additionally, the paper also discusses proactive decision-making through predictive analytics, which reduce the cost of turnover and improve the organization to become more agile. Nonetheless, the ethical considerations, data quality, regulatory requirements, and employee resistance are the challenges that have to be overcome so that maximum benefits can be achieved. One of the AI and analytical study findings emphasizes the role of AI and analytics as strategic enablers rather than operational tools that enable the HR to push the competitive advantage and drive the issues of transparency, inclusivity, sustainable growth, etc. The paper ends with explanations of how AI in HR could be incorporated using sound ethical governance, data management and adaptation to industry needs to attain organizational stability and financial stability in the long run.

Family efficacy refers to a family’s belief in its collective ability to achieve desired outcomes. It emphasizes the extent to which family members—parents, children, and spouses—can work together by pooling their resources, skills, and efforts. A family functions as a system with unique rules, roles, communication patterns, values, trust, faith, sacrifice, acceptance, and responsibilities that extend beyond individual members.

According to Kao and Caldwell (2017), family efficacy reflects the belief and confidence that family members can positively impact their children’s education, highlighting the role of parental involvement both at home and within the school environment.

Index investing has long been popular in developed markets and is now gaining significant traction in developing economies like India, as investors seek cost-effective, diversified investment options. While traditional index funds provide broad market exposure, momentum investing gives an added edge by capitalizing on market trends, offering the potential for enhanced returns. Though momentum strategies are not ideal for risk-averse investors or those with short-term financial needs, they can serve as a high-growth component in a well-diversified portfolio for those comfortable with periodic rebalancing. This study examines the performance of momentum investing compared to traditional market indices, highlighting how momentum indices like Nifty 200 Momentum 30 and Nifty Midcap 150 Momentum 50 consistently outperform benchmarks such as Nifty 50, Nifty Midcap 150, and Nifty 200 over three-year and five-year rolling periods. While momentum investing offers higher returns, it comes with increased volatility, making it more suitable for investors with a long-term horizon and a higher risk appetite. A t-test assuming unequal variances confirms that the superior performance of momentum indices is statistically significant, indicating that the excess returns are not due to chance. Additionally, over a five-year period, momentum indices never recorded negative rolling CAGR, unlike broader indices, reinforcing the importance of staying invested for the long term.

Decision-making has changed a lot in this digital age. This increase in UGC on social media affected tourism because people increasingly relied on social media reviews for travel plans. Today, when planning a trip, most travellers rely upon the opinions and experiences of fellow travellers on the Web. The reason is the large number of UGCs on various social media platforms like Instagram, X, Facebook, and YouTube. Travel blogs, videos, photos, and reviews, among others, are shared by travellers in real-time, and the information can be quickly accessed by other people with internet access. It is important to note that social media reviews greatly influence other people's travel decisions. No matter what age they are, people believe in the opinions of others more than they believe in any form of advertising. In this context, previous studies have found that most travellers like to read reviews before deciding. User-generated content provides authenticity and transparency, which advertisements cannot portray. People can analysed feedback from fellow travellers to decide on their travel plans. Though subjective, other people's opinions and experiences are very informative regarding places, attractions, hotels, and activities one may want to visit or experience. Besides, connecting with other travellers through social media nowadays is very easy, and it helps a person make inquiries and ask for recommendations from real people who have undergone the same experience. Therefore, UGC posts on social media are very crucial in decision-making. Data was collected from 100 youngsters in India and analysed using SPSS software to determine the role of user-generated social media content. Factor analysis results identified 3 factors that influence decisions: Trust and credibility, Risk, and ease of use. The main objective was to identify the types of user-generated content that influence adventure travel decisions among youngsters and to know young traveller’s most commonly used social media platforms for adventure tourism information. 

The rising frequency of business insolvencies underscores the critical need for advanced predictive tools that offer real-time risk assessment. Traditional bankruptcy prediction models, which primarily depend on retrospective financial statement data, often fail to provide timely insights due to reporting delays and data incompleteness. This study proposes a machine learning-based framework for proactive bankruptcy prediction, leveraging ensemble learning techniques for greater accuracy and reliability. The analysis draws on a robust dataset comprising 2,800 firm-year observations from companies listed on the Bombay Stock Exchange (BSE) over the period 2020 to 2025. Quarterly financial indicators and market-based variables were systematically compiled to ensure comprehensive coverage of firm performance. To enhance model performance and mitigate overfitting, three advanced ensemble algorithms—XGBoost, AdaBoost, and Random Forest—were implemented. These models combine bagging and boosting mechanisms to optimize predictive capabilities. Recursive Feature Elimination (RFE) was used to identify the most significant predictors influencing financial distress. The study empirically tested four hypotheses: liquidity constraints as indicators of distress; operational cash flow deficits correlating with default risk; high debt-to-equity ratios signaling instability; and declining return on assets (ROA) as an early warning sign. The results demonstrate that the ensemble-based approach delivers high classification accuracy, with key variables conforming to recognized financial risk indicators. The findings highlight the potential of machine learning models to serve as early detection tools, aiding firms and investors in strategic decision-making. This research emphasizes the transformative role of artificial intelligence in improving financial risk management systems, enabling more dynamic and informed responses to emerging threats of bankruptcy.

The practice of financial organisations implementing socially and environmentally responsible strategies is known as "green banking." It entails offering financial services and products to promote eco-friendly projects. Green banks use energy-efficient operations and reduce paper waste, among other measures, to lessen their environmental effect. By incentivising consumers and businesses to adopt eco-friendly activities, they want to encourage sustainability. Ultimately, green banking unites the banking industry with initiatives to mitigate climate change and save natural resources. (Karthikeyan et al.,2018) Green banking practices are defined as electronic goods and services promoting environmentally friendly activities, such as electronic waste management systems, power consumption reduction, and eco-friendly operations. (Ganesan et al.,2016) Although the banking industry is not thought to be polluting, change is urgently needed. Green banking is a broad approach that integrates environmental factors into every facet of the banking sector. This study has taken Scopus as a citation database to conduct the bibliometric analysis.