FLAME University is a private, coeducational and fully residential liberal education university located in Pune, India. It was formerly known as FLAME - Foundation for Liberal and Management Education.
PurposeToday, the metaverse is gaining prominence as a virtual space for forthcoming technological transformation, but its mass adoption remains limited. Its widespread adoption is hindered by several barriers, which affects it integration in the daily use. The study aims to identify and rank the barriers by providing comprehensive insights into their interconnected influences.Design/methodology/approachThrough the extensive literature review, 10 significant barriers were identified. Thereafter, the responses were gathered from 32 experts from academia and industry through a structured questionnaire. Later, grey influence analysis (GINA) was applied to rank the most significant barriers.FindingsThe findings reveal that privacy and security concerns, integration and interaction challenges and ethical and legal constraints are the top-ranked barriers. However, speed and cost constraints, tech-triggered health issues and environmental concerns were found to exert least influence. The GINA model further determines the cause-effect relationship among barriers by showing high-impact factors trigger cascading effects on other adoption challenges.Research limitations/implicationsThis study's results are generalized, and they may differ across different sectors, industries and user demographics. Future research can work to examine longitudinal and sectoral variations in adoption behavior.Practical implicationsThis study offers an advice to policymakers, platform developers and educators for addressing main issues like investing in privacy protection, digital literacy campaigns and inclusive policy framework to reduce the entry of barriers.Originality/valueTo the best of our knowledge, this study is the first one to apply GINA for the metaverse adoption by offering a systematic understanding of metaverse adoption barriers like how they influence each other. It gives a unique order of barrier influence, contributing a new systematic perspective to metaverse adoption research.
PurposeThe global Fintech revolution has reshaped financial services, promising unprecedented innovation and financial inclusion. Yet, beneath rapid technological advancements lies a persistent gender imbalance, with women continuing to occupy fewer than 10% of leadership roles within global fintech firms and only 5% in Indian Fintech start-ups and established firms. What are the barriers preventing a greater participation of women in the Indian Fintech sector? This study aims to draw on interviews with multiple Indian women Fintech leaders to understand the barriers preventing their equal participation and growth in the sector. Design/methodology/approachDrawing on an original qualitative dataset of nine in-depth semi-structured interviews with senior women leaders in India’s fintech sector, this paper uses purposive sampling and systematic thematic analysis to investigate the biases and barriers they encounter. The analysis identifies three intersecting challenges – (1) access to capital, (2) perceptions of leadership capability and (3) exclusionary professional networks, and highlights the progressive steps taken by these women in fintech organisations to address some of these biases. By centering a small, hard-to-access group of women in fintech leadership, the study offers a distinctive empirical contribution to scholarship on gender and leadership in financial technology. FindingsThe interviews highlight the deeply embedded structural and cultural barriers facing women in the fintech sector including 1) exclusion from informal networks, 2) lack of visible role models and 3) investor bias leading to limited access to capital. Originality/valueThe paper adds to the growing body of literature on women’s entrepreneurship in India. It invites scholars to go beyond the simplistic narrative of fintech as a democratising force, and to focus more on the institutional, cultural and financial challenges faced by women entrepreneurs in the Indian fintech sector in particular, and in the Global South in general.
Effective family-school engagement (FSE) is key to improving student outcomes. This study in Tripura, India, used the Conversation Starter Tools (CST) to explore FSE practices among parents and community leaders (N = 216), teachers (N = 186), and students (N = 196). FSE was analyzed across three domains: communication with schools, supporting learning at home, and staying informed about school activities. T-tests and ANOVA revealed a misalignment between teacher and parent views on the purpose of education. Key barriers included financial constraints, time, distance, limited awareness, and reduced interest. Findings highlighted ongoing communication gaps and the need for mutual respect and understanding. This study highlights the need for targeted tools in culture-specific contexts to address parent-teacher misalignment and promote equitable educational outcomes for students.
Accurate rain rate estimation remains a longstanding challenge in atmospheric science, with significant implications for disaster management and agricultural planning. In this work, we present a deep learning-based approach utilizing a conditional Generative Adversarial Network (cGAN) to estimate rain rates from satellite-derived Outgoing Longwave Radiation (OLR) data in the region from latitudes 0◦N to 40◦N and longitudes 60◦E to 100◦E, i.e. the Indian subcontinent and its surrounding regions. Quantitative evaluation across multiple rain rate thresholds over a five-year period demonstrates the competitiveness of our method compared to traditional algorithms such as Hydro-Estimator (HE) and INSAT Multispectral Rainfall Algorithm (IMSRA), particularly in detecting moderate to heavy rain events. The threat scores for the proposed method range from 0.377 at 0.5 mm/hr to 0.021 at 20 mm/hr, compared to 0.265 and 0.024 for HE and 0.270 and 0.023 for IMSRA. Thus, the proposed method results in substantial improvements of more than 35
PurposeThere is a lack of studies exploring how artificial intelligence (AI) enables operational excellence, which justifies the successful integration of AI and how it can be connected to circular economy (CE). This study aims to examine how AI-driven operational excellence enables resource utilization to facilitate the CE transition.Design/methodology/approachIn total, 12 enablers were identified through literature, and using the Fuzzy-DEMATEL technique, their cause-effect analysis and prominence rating are conducted.Findings"Improved transparency, coordination and trust", "improved forecast of demand and other uncertain supply chain (SC) parameters", "accurate real-time information flow", "decision support for specialized CE-based business models" and "improved eco-accounting of SC impacts" are identified as the most prominent enablers.Originality/valueThese enablers will help enterprises identify specific use cases of AI in CE-based business models, thereby accelerating adoption and improving resource utilization and circularity. The study's findings will assist managers and practitioners in understanding the operational aspects of how AI contributes to enhanced resource circularity.
