The International Insurance Law Review (IILR) is a prestigious academic journal dedicated to advancing the understanding of insurance law across jurisdictions. Renowned for its rigorous standards and commitment to quality, IILR serves as a global platform for scholars, practitioners, and policymakers to explore and discuss critical issues in the field of insurance law. International Insurance Law Review ISSN: 0968-2090 Publisher: Lumar Publications Email: editor@lumarpub.com Indexed by: Australian Business Dean Council (ABDC) - A Rating
Is Academic excellence in Diversity & Inclusion, Competency-Based Learning, and AI-Driven Learning influenced by Wellness and Institutional Support and faculty engagement?
The evolving landscape of higher education are increasingly influenced by the aspects of Diversity & Inclusion (D&I), Competency-Based Learning (CBL), and AI-driven Learning (AI_LS) to promote academic achievement. This study investigates if success in education is dependent on Emotional Well-Being (EW), Institutional Support & Faculty Engagement (IS&FE). Using SEM and SmartPLS 4, the data comprising higher education students’ responses was analysed to understand such interdependencies. Results indicate that AI_LS increases the Academic Performance (AP) significantly (β = 0.515) due to their positive effect on Engagement in Learning (EW) (β = 0.363). CBL has a positive contribution to engagement as well (β = 0.146), while D&I has a lower direct effect (β = 0.109), and no indirect impact on academic performance. The IS&FE moderator in some cases was weak and negative, meaning that insufficient institutional support hindered proper learning. The validity of the model is confirmed by the model fit indices, although there adequacy of AI-based metrics is questionable (AI_LS = 0.453). These results imply that D&I, CBL, and AI learning are part of academic performance drivers, but their impact is determined by the degree of institutional support and wellness initiatives integration. This study provides insight into how strategic faculty involvement and institutional alignment can effectively enhance AI-enabled learning and competency-based Learning education.
Competency-Based Learning, AI-Driven Learning, Diversity & Inclusion, Emotional Well-Being, Institutional Support, Higher Education, Structural Equation Modelling (SEM).
Copyright (c) 2025 International Insurance Law Review https://creativecommons.org/licenses/by-nc-nd/4.0
Building AI Capability for Organizational Innovation: The Mediating Role of Knowledge Sharing
With the increased trend in digital transformation, efforts are now being strategically invested in the use of artificial intelligence (AI) and being explored by businesses that should promote the experience of innovation and the survival of enterprises over the long run. In the given research plan, we respond to the way the rise of the capability of AI is related to the knowledge-sharing activity, with a sophisticated consideration of the mediator of knowledge-sharing activity, that is, the nature of the innovation potential of an organization. Based on the theory of the Knowledge-Based View (KBV), the work proposes and empirically validates a model that interconnects the manifestations of AI training programs, the drivers of knowledge-sharing, and the resultant innovation. Survey data of 312 professionals working in technology-led organizations was analyzed by means of Partial Least Squares Structural Equation Modeling (PLS-SEM). The results also indicated that the development of the competency in AI will have a positive impact on the knowledge sharing and on the innovative performance, which will be further enhanced. It has also been understood that the outcome of knowledge sharing has a moderate mediating impact on the correlation between the building of this AI capability and innovation, which in turn means that it demonstrates a huge impact in the process of transformation of technical expertise to the innovation outcome, as each of these two is concerned. Such an addition to the field of knowledge about the AI-based skill formation and organization learning processes determines how the operations of the knowledge-sharing initiatives can be utilized to combine the power of AI to enable innovation. The given insights may be regarded as useful advice to the leaders and policy-makers who may be interested in improving innovations by adding a certain set of AI training and knowledge management procedures.
AI Capability, Knowledge Sharing, Organizational Innovation, Digital Transformation, PLS-SEM, Knowledge-Based View, AI Training
Digital Insurance Platforms: Legal and Ethical Challenges in Consumer Data Protection
The rapid expansion of digital insurance platforms has transformed the insurance industry by streamlining policy issuance, premium collection, and claims management through data-driven automation. However, this technological shift introduces profound legal and ethical challenges in the domain of consumer data protection. Insurance companies increasingly rely on big data analytics, artificial intelligence, and blockchain-enabled ecosystems to personalize risk assessment and pricing, which in turn raises concerns about data privacy, algorithmic bias, regulatory compliance, and consumer rights. This study critically examines the evolving landscape of digital insurance with a focus on the tension between innovation and consumer protection. Using a mixed-method approach that integrates a legal-analytical framework with comparative case studies across leading jurisdictions (EU GDPR, India’s Digital Personal Data Protection Act 2023, and the U.S. sectoral regulations), the paper identifies gaps in enforcement, ethical dilemmas in consent management, and risks of discriminatory profiling. Findings reveal that while digital insurance enhances accessibility and efficiency, the opaque nature of algorithmic decision-making and cross-border data transfers exacerbate vulnerabilities for consumers. The study argues for a balanced framework that harmonizes technological innovation, legal safeguards, and ethical accountability, ensuring that digital insurance evolves as a trusted, transparent, and consumer-centric financial service.
Digital insurance, Consumer data protection, Legal challenges, Ethical issues, Privacy regulation, Algorithmic bias
Future of Skilled Indian Immigrants in Canada: Exploring the Impact of Immigration Policy, Economic Fluctuations, and Educational Investments on Gender Disparities
Objective: The focus of the paper will be on the integration of the skilled Indian immigrants in the Canadian labor market with articulation along the four key dimensions, which include migration trends, gender disparity in employment outcomes, the significance of investing in post-secondary education and the effect of the recent immigration policies. The major goal is to be able to know why highly qualified Indian women still encounter disparate labour market outcomes even with the same qualification with that of women in other nations and to estimate the trends of these trends with time. Methodology: As a research method, a quantitative approach is employed by the usage of secondary data that is obtained in Canadian Census, Labour Force Survey, and even in Immigration, Refugees and Citizenship Canada (IRCC) reports. Some of the statistical methods that will be used are descriptive statistics to get an idea on the distributions of variables, regression analysis to get an idea on the relationship between variables such as education, gender and employment type and trend analysis, to get an idea on the changes since 2020 and till 2024 and predictive modeling, to get an idea on how the labor market participation and employment outcomes are going to be in the next decade. Key Findings: There is a consistent/continued gender gap on part of employed skilled Indian immigrants where women are likely to be underemployed, occupying deskilled jobs. Relevant policies in Canada to assist immigrant medical workers have perfectly aligned and thus streamlined entry options, although the job opportunities are stained by a systemic discrimination result. The benefit of educational qualifications obtained in India are not always valid in Canada thereby increasing the disparity in the labor market. Trend and predictive models have shown that poor and middle-income families would continue to dominate gender-based disparities unless policy and institutional reforms are undertaken to change these trends in the next several years.
Skilled Indian immigrants, Canada, gender disparities, immigration policy, labor market integration
Ecotourism as a sustainable livelihood (A case study in Bongaigaon District of Assam)
Ecotourism is a nature-based tourism in which tourist can experience a region’s natural resources, its flora, fauna, culture, and the social practices of local communities. It contributes to national development by generating revenue from the visitors and also support the conservation of natural environment. Bongaigaon District of Assam is located in the biodiversity hot-spot. Hence, the study region is rich in various ecotourism spot. These include reserve forest, proposed sanctuary, sacred grooves, temples and wetlands. Tourism in Bongaigaon District basically falls under six categories. These are rural tourism, Wildlife tourism, religious tourism, cultural tourism, adventure tourism and river tourism. Famous spots are Kakoijana Reserve Forest, Nakkati Pahar, Sanyashi Pahar, where 10 numbers of sacred grooves exist. Although the district is quite rich in ecotourism sites, inadequate road connectivity, limited community awareness, communication gaps, and a lack of government initiatives have prevented it from attracting visitors from across the world. It also reflects on necessity of suitable tourism laws to protect the tourist hot spots as well as on the need of insurance, particularly for adventurous tourism.
Ecotourism, Flora, Fauna, Hotspot, Sacred grooves.
Decoding Redditors Emotional Cues: A Naïve Bayes Theorem Unveils the Hidden Stress of Subscribers Through Sentiment Analysis
Social media is gradually replacing in-person interactions in communication including sharing information and opinions, describing experiences, and updating others on daily activities. Virtual communication is replacing real-world communication in certain ways. Reddit is a social news website and forum where users may vote on, comment on, and share items, it is a conversation site. It is divided into subreddits, or communities centered around specialty subjects such as technology, politics, or more. The massive number of posts, content, comments, and discussions on different specific topics provides a chance for behavioral research and sentiment analysis on humans. The Redditor's behavior and the emotional cue represent an enormously expansive and mostly unknown research field. Stress detection is one of these fields. This paper's primary goal is to conduct the sentiment analysis using the Naïve Bayes approach to predict the stress level of Redditors based on the posts and comments they shared in the stress community on their social platforms. The secondary goal is to help manage stress and reduce negative emotions; we recommend exploring various coping strategies. To sum up, the detection rate of Naïve Bayes prediction is quite good. The results of this research are beneficial to one's well-being.
The Role of Mediation in Addressing Transnational Human Rights and Public Interest Disputes
Cross-border human rights and public interest disputes have grown more visible and complex in recent decades. These conflicts usually involve overlapping jurisdictions, culturally diverse parties, and actors with unequal bargaining power. Formal adjudication, whether before domestic courts or international tribunals—remains essential, but it is increasingly criticized for being slow, rigid, and adversarial. As a result, parties often struggle to obtain timely or meaningful remedies. In this context, mediation is emerging as a promising complementary mechanism. Unlike traditional litigation, mediation allows disputants to engage in structured dialogue, explore creative solutions, and acknowledge social, cultural, and emotional dimensions that courts may overlook. This paper critically examines the utility of mediation in transnational human rights and public interest matters. It highlights the theoretical, normative, and institutional foundations that support its use, including the UN Guiding Principles on Business and Human Rights (UNGPs), the practice of independent accountability mechanisms (IAMs), and the growing influence of the Singapore Convention on Mediation. It also uses comparative case studies to illustrate both the potential of mediation and the challenges associated with power imbalance, legitimacy, and enforcement. The paper argues that mediation, when conducted with strong safeguards and anchored in human rights, can provide restorative, participatory, and forward-looking outcomes. However, its effectiveness depends on institutional integrity, meaningful participation by affected communities, and cross-border enforceability of settlements.
Mediation, Transnational Disputes, Public Interest, UNGPs, Human Rights, IAMs
Leveraging FinTech for Trade Competitiveness: Evidence from India
This study examines how FinTech-related structural factors influence India’s international trade flows using annual data from 2010-2023. Employing time-series regressions with standard diagnostic tests, the analysis evaluates the role of FinTech through two channels: domestic market size and foreign direct investment (FDI). The results show that FDI has no significant effect on export or import growth, whereas changes in domestic market size exhibit a strong, statistically significant positive relationship with both trade components. These findings suggest that FinTech enhances trade primarily by expanding and formalizing the domestic market through digital payments, SME credit access, and efficient financial infrastructure. The study underscores FinTech’s role as a trade-enabling ecosystem supporting India’s global integration.
Legal Implications of Climate Change and ESG Reporting on Insurance Policies
The paper examines what is a complex development between climate change, the emerging Environmental, Social, and Governance reporting policies and the legal consequence of the same in the profoundly integrated insurance business in India. It also examines the effect of these forces in driving the necessity of reconsidering risk assessment, policy underwriting and the overall regulatory structure that is relevant within the Indian insurers particularly in a context of global sustainability initiatives and domestic case law trends. The increased frequency and severity of climate-related events are also a part of situations discussed in the paper which is prompting insurers to consider ESG as a core operation that affects policy formulation and underwriting strategies to respond to the emerging risk and opportunities that are obviously emerging. Such integration is significant as the stakeholders pressuring insurers to act with seriousness towards the growing ESG risks that do not only represent the impact of climate change but also other societal risks, including inequality and the risk of governance, such as fraud. Such an intensive survey should be aimed at analyzing the legal grounds that are slowly forcing the ESG integration in the Indian insurance landscape, both nationally and devotion to the global reporting standards.
ESG, Climate Change, Insurance Law, India, Sustainability.
A Study of Gen Z Buying Behaviour for FMCG Products
Fast-moving Consumer Goods (FMCG) form a core part of daily consumption behaviour, especially among Generation Z consumers, who are highly informed, tech-savvy, and preference-driven. This study investigates the major determinants influencing their buying behaviour, particularly across three key factors: Perceived Convenience & Accessibility, Assortment & Price Promotions, and Trust & Quality Assurance. Additionally, the research examines how these factors influence consumer purchase intention and channel preference across retail formats such as grocery shops, malls, and online platforms. With changing lifestyle patterns, digital influence, and rising brand consciousness, Gen Z has emerged as a distinctive market segment requiring strategic attention. The study highlights a growing shift in consumer expectations toward convenience, wide product availability, and quality assurance. Retail choice is increasingly determined by the combined effect of these variables rather than individual elements alone. Therefore, the findings will assist retailers, marketers, and FMCG companies in designing more targeted marketing campaigns, optimizing retail channels, and enhancing consumer satisfaction to stay competitive in a rapidly evolving market landscape.
Generation Z, FMCG, Buying Behaviour, Grocery Shops, Online Shopping
Macroeconomic Drivers and Risk Behaviour of India’s Sustainability Indices
In recent years, sustainable investing has moved from an ethical niche to a key pillar of global finance. Investors today look beyond profit alone, seeking portfolios that align with environmental and social goals. In this setting, understanding how sustainability-focused indices respond to economic shifts is crucial—especially in fast-growing markets like India. This study examines the relationship between three macroeconomic indicators—inflation (CPI), money supply (M3), and the exchange rate (INR/USD)—and the performance of India’s two leading sustainability indices, the S&P BSE Carbonex and S&P BSE Greenex, during the period November 2012 to March 2024. The analysis employs the Autoregressive Distributed Lag (ARDL) framework along with the Error Correction Model (ECM) to explore long-run equilibrium and short-run dynamics. To capture volatility behaviour, GARCH, EGARCH, and TGARCH models are applied, and a CAPM-based comparison evaluates the risk–return performance against market benchmarks. Findings confirm a stable long-run relationship between the sustainability indices and the selected macroeconomic factors. Inflation and exchange rate depreciation reduce index returns, while money supply growth enhances them. Volatility is persistent and asymmetric—negative shocks amplify market swings more than positive ones. The Greenex appears more stable than the Carbonex. Overall, the results show how India’s sustainable investments move with broader macroeconomic trends and highlight the need for steady monetary and exchange rate policies to foster a resilient green financial market.
Sustainability indices; Inflation; Money supply; Exchange rate; ARDL model; Error correction model (ECM); GARCH-family models; Volatility; CAPM; Carbonex; Greenex; Sustainable finance; India; Emerging markets
Navigating the New Frontier: Regulatory Challenges and Compliance in India's AI-Driven Insurance Transformation
The fast pace of digitalization and expansive utilization of the Artificial Intelligence (AI) create an earthquake in the Indian insurance market. This reform is projected to make the national vision of Insurance to all people through an achievement by 2047 and bring efficiency on the level that has never been attained before, higher penetration of the markets as well as higher experiences by the customers. But it is also an environment of tough and dynamic regulation matters that comes along with the technological breakthrough. The current paper is a critical reflection of the main regulation and compliance challenges of the Indian insurance sector with regard to the implementation of AI and digital technologies. It talks about the interrelation between the rule of Insurance Regulatory and Development Authority of India (IRDAI), Digital Personal Data Protection Act (DPDP Act) 2023, and the system of AI regulation on the national level developed by the Ministry of Electronics and Information Technology (MeitY). Among the most pressing issues, it is possible to mention the preservation of the privacy of information, the absence of the prejudice of algorithms, the creation of rigid accountability/liability boundaries, and enhanced measures of cybersecurity. The postulation of this paper is that to exist in this new frontier is to be alive and not as southern watch keepers but as one to ingrain in their very genes the postulates of responsible AI of elements of fairness, transparency and accountability. Through regulatory sandboxes, effective internal regulation, and strong cooperation with policy-makers, Indian insurers will be in a position to tap the entire potential of AI and the establishment of an insurable core of trust, safety, or inclusion as their next insurance ecosystem.
Insurance-Driven Risk Mitigation and Technological Integration in Deep-Tier Supply Chain Finance
This study examines the use of insurance players in deep tiers of supply chain finance (SCF) as a way of risk mitigation and efficiency improvement in technology. This study links up three fundamental questions: minimizing the financial riskiness of banks and SCF platforms by covering it with insurance, assessing the insurance structures based on different models that accommodate supply chains, and assessing the impact of technology in enhancing the efficiency of insurance in deep-tier settings. Methodology was a quantitative method where a sample size of 150 companies across different levels was analyzed using descriptive statistics, regression models, correlation matrices, and scenario stress tests. Findings suggest that a higher insurance coverage is much more important as it decreases the level of default (beta = -0.28 and p < 0.01), particularly in the presence of high rates of technological adoption. Parametric insurance is better than old models, and there is the lowest technological integration amongst the deep-tier suppliers, which is correlated to the risk. The paper also concludes that instituting InsurTech within SCF is critical in de-risking as well as resilience.
Supply chain finance, Insurance, Risk mitigation, InsurTech, Deep-tier financing
Financial Distress Prediction in Indian Small-Cap Companies: A Longitudinal Analysis Using Altman’s Z-Score Models
This study examines financial distress dynamics in Indian small-cap firms using Altman's Z-score and Z''-score models through longitudinal analysis of 30 NSE listed companies observed semi-annually from March 2019 to March 2025. The research addresses critical gaps in emerging market distress prediction literature while capturing the complete COVID-19 pandemic trajectory. Results reveal significant heterogeneity in small-cap financial health: Z-score classifications show 60.5 per cent Safe, 18.5 per cent Grey Zone, and 21.0 per cent Distressed firms, while Z''-score identifies fewer distressed companies (14.9 per cent), yielding moderate inter-model agreement (Cohen's κ = 0.493). The Z-score exhibits 3.8-fold greater temporal volatility than Z''-score due to sales-to-assets ratio sensitivity. Temporal analysis demonstrates a pronounced V-shaped recovery pattern, from pre-pandemic vulnerability (28.3 per cent distressed) through pandemic crisis peak (36.7 per cent distressed, March 2020) to robust recovery with 61.7 per cent improvement by March 2025. Sectoral analysis reveals statistically significant heterogeneity (ANOVA: F = 15.87, p < 0.001), with defensive sectors (FMCG: 0 per cent; Consumer Durables: 3.8 per cent) contrasting sharply with infrastructure sectors (Power: 100 per cent; Telecommunications: 76.9 per cent). Sector classification explains 32 per cent of distress variance. Findings advance bankruptcy prediction theory through emerging market validation and inform practice via dual-model investment screening, semi-annual credit monitoring and sector differentiated risk assessment. The study challenges conventional small firm fragility assumptions, demonstrating substantial adaptive capacity and resilience among survivors.
Financial distress prediction, Altman Z-score, Small-cap companies, Longitudinal analysis, Bankruptcy prediction
Corporate Social Responsibility and Financial Performance: the “virtuous circle” revisited in India
This study empirically analyses the causal relationship between corporate social responsibility disclosure (CSRD) and financial performance (FP) of firms in the emerging economy of India.Content analysis of CSR disclosure is conducted in annual reports of the sampled firms to create a multidimensional measure of CSRD based on stakeholder theory. “Granger causality” method is employed to examine the direction of causality between the aggregate CSRD measures and FP, and the six segregate CSR dimensions and FP. Multiple regression analysis is employed to examine CSRD-FPlinkage controlling for the confounding effects of size, risk, age, industry and ownership.The results of this study show that there is statistically significant unidirectional “Granger causality” from FP to aggregate CSRD andtwo of the six CSRD dimensions of Indian firms. This study contributes to the scarce literature on CSRD-FP linkage in context of emerging economies by using a more inclusive data set, creating a reliable measure of CSR disclosure applicable to a large universe of firms, and including relevant control variables that affect CSRD-FP relationship.
Corporate social responsibly, Financial performance, ROA, ROE, Granger causality
Hyper Personalization in Wealth Management powered by Medallion Architecture
Hyper-personalization is transforming the Wealth Management industry, enabling Financial Advisors to reach their clients more effectively with the help of innovative technologies, such as artificial intelligence (AI), machine learning (ML), and the Medallion Architecture. The role of financial advisor(s) would be the first casualty of the technology. Autonomous robo-advisors are increasingly threatening to replace human financial advisors. This paper, therefore, examines how a Financial Advisor can put to work Medallion architecture, a scalable data lakehouse platform, to offer customized financial services to the affluent sector (requiring assets of between 1 million and 5 5million dollars) by processing large volumes of data, including transaction logs, market feeds, behavioral data, and lifestyle preferences. The Bronze, Silver, and Gold levels of the Medallion Architecture enable the creation of an effective data pipeline, facilitating data integrity, scalability, and cost minimization through the use of tools such as AWS S3, AWS Glue, and Databricks. By optimizing the process of data ingestion, transformation, and enrichment, this paper explains how the computational costs of hyper-personalization and advisor margins can be balanced, especially with mass affluent and affluent clients. It suggests the use of such strategies as Change Data Capture (CDC), parallel processing, and micro-batch methods, which would increase the performance without violating compliance and regulations. The article will present the practical consequences of using AI-based predictive analytics and multi-channel client interaction systems to provide scalable, context- sensitive financial advice and, thus, promote trust and customer loyalty within a competitive online ecosystem.
Dataware House, Medallion Architecture, Extract Transform Load (ETL), Extract Load Transform (ELT), Data Value Chain, Wealth Management, Hyper Personalization, Cost Optimization.
Acceptance of Chatbot in government services: An analysis of responses from 1,00,000 actual users of chatbot during covid vaccination drive in West Bengal
The objective of this research is to identify key factors influencing the use of chatbot for healthcare services by its actual users. This research uses the relative advantage, ease of use, perceived usefulness, perceived risk and time to understand user intention to use chatbots for healthcare services. The puzzle of using AI based chatbots for healthcare services needs to be addressed as there are many issues of delivering healthcare services. Many of the difficulties faced are people’s lack of knowledge on using a technology for availing healthcare services, literacy, intention to use, awareness, reach etc. This study has focused on the key factors influencing users behavior derived from innovation diffusion theory (IDT) and some factors from technology acceptance model (TAM) as they focus on key usage intentions. This study looks at the user perspective of using a technology driven service such as a Chatbot. A survey method was used to get the responses of the people in using Chatbot. A questionnaire was derived from the user acceptance theory and related theories and modified to suit the need of this research. A total of 100,000 responses were collected on various aspects of using chatbot service for vaccination during Covid-19. More than 1,00,000 people actual users responded. So, their responses can be taken into consideration for designing such future technology driven healthcare administration. This research has come up with the factors that has highly impacted covid vaccination drive during the time when everything was uncertain and health administration was also under extreme pressure to handle such pandemic situations.
A review of India's AB-PM-JAY including strategic purchasing, decentralized regulation and financial risk protection
India's Universal Health Coverage (UHC) mission is one of the most ambitious policy efforts in global health governance. It aims to give everyone equal access to quality health care without putting a strain on their finances. The Ayushman Bharat–Pradhan Mantri Jan Arogya Yojana (AB-PM-JAY), which started in 2018, is the main part of this project. It is a big step towards making healthcare financing more accessible because it offers health insurance coverage of up to ₹500,000 per family per year to almost 500 million people. This review brings together the most recent evidence on how well AB-PM-JAY works and how it is run, focussing on strategic purchasing, cost monitoring, state-level implementation models, and the changing structure of "decentred regulation." Empirical evidence consistently shows that AB-PM-JAY significantly lowers out-of-pocket costs (OOPE) for tertiary procedures like heart surgeries. However, there are still big differences between states in how long it takes to process claims, how efficiently the government runs, and how well costs are covered. This study finds big gaps in research in areas like integrating dynamic cost surveillance, comparing the performance of different state models, regulatory pluralism, and the area of oral healthcare financing that hasn't been investigated enough. It sets research goals and hypotheses based on theoretical and real-world evidence, and it suggests a statistical framework that includes descriptive, correlational, and inferential analyses to help with real-world testing. The discussion includes important measures like the average decrease in OOPE, the relationship between cost monitoring and claim turnaround time, and the use of regression to predict OOPE. The synthesis results show that for sustainable UHC, it is very important to include cost surveillance in digital health infrastructure, improve hybrid purchasing models, and set up clear regulatory coordination mechanisms. The paper ends by saying that AB-PM-JAY is a huge step forward in protecting people from financial risk, but its long-term success depends on pricing that is based on evidence, strong governance, and making sure that all health areas, including oral care and preventive services, are included fairly.
Federated TinyML for Privacy-Preserving Health Data Analytics on Edge Devices
Healthcare data is inherently sensitive, and deploying machine learning solutions often raises concerns about patient privacy and compliance with data protection regulations. This paper proposes a novel framework combining TinyML and Federated Learning (FL) for privacy-preserving health data analytics at the edge. By training lightweight models locally on wearable and IoT devices, and aggregating insights through federated protocols, our approach minimizes the need for raw data sharing while ensuring robust performance. We implement federated TinyML pipelines for detecting cardiac irregularities and stress levels using physiological data, with an emphasis on minimizing communication overhead and energy consumption. Experimental evaluation demonstrates accurate detection of cardiac irregularities using high-quality physiological signals, such as photoplethysmography (PPG) and electrocardiogram (ECG). The pipeline achieves near-cloud-level accuracy while significantly reducing communication overhead, energy consumption, and inference latency on edge devices. Results show that the system effectively supports personalized healthcare interventions. By combining federated learning, TinyML, and healthcare-specific evaluation metrics, the proposed framework provides a low-power, scalable, and privacy-preserving solution for real-world deployment in digital health platforms and personalized medicine applications. The proposed system provides a scalable solution for privacy- preserving, low-power healthcare AI, offering strong potential for real-world deployment in personalized medicine and digital health platforms.
Healthcare, Machine Learning, Federated, Edge devices, Internet of Things.
Environmental Consciousness and Consumer Perception as Determinants of Market Accountability: A Mixed-Methods Model of Sustainability Attitude Formation and Behavioural Intention
Purpose: This paper will explore the association between environmental awareness and consumer perception, with the emphasis of determining the effect of sustainability awareness on consumer behaviour and decision-making. Particularly, the analysis determines the influence of the attitude towards sustainable brands and sustainable brand products, based on the degree of environmental consciousness- as the tool of analysis in the perception of consumers in the framework of sustainable practices. Methodology: The mixed-methods approach will be used in the study to integrate quantitative and qualitative methods to gain in-depth understanding of consumer psychology. Online survey will help in capturing consumers perception and behavioural responses, whereas semi structured interviews will give a deeper qualitative insight. The causal relationships between the most significant variables are tested with the help of the regression analysis based on Smart PLS, and thematic analysis of the interview data demonstrates the motivational and perceptual factors that stimulate the eco-conscious consumer behaviour. Findings: The results prove the existence of a strong correlation between environmental awareness and consumer image indicating the importance of psychological and attitudinal determinants in influencing sustainable buying behaviour. Combining statistical and thematic knowledge, the study offers a comprehensive knowledge of the consumer internalization process of sustainable practices and its subsequent conversion in behavioural performance. Practical Implications: The findings provide practical suggestions to brands and retailers that want to enhance their sustainability in positioning. Through the knowledge of how the environmental consciousness reflects on consumer perception and behaviour, organisations will be in a position to structure their marketing strategy, product development and sustainability programs along with consumer values thus creating trust and long-term interests with the consumers. Originality/Value: This research has a contribution on the theoretical and empirical discussion of green practices because it attributes environmental awareness to consumer perception and behaviour using a psychological approach. The dual-method analysis and integrated design offer a solid structure of studying the formation and operationalization of sustainability attitude and have significant implications to scholars and practitioners of sustainability-related market research.
Environmental Consciousness; Consumer Perception; Consumer Behaviour; Sustainable Practices; Psychological Drivers; Market Accountability; Sustainability Attitude Formation
The Metaverse and the Rule of Risk: AI Insights, Human Judgment, and the Future of Digital Governance
The appearance of the metaverse is changing the business and education ecosystem, and has brought new legal, ethical, and regulatory issues in the areas of risk governance and insurance law. The given work is a comparative analysis of the views of the experts human opinion and AI-generated in order to investigate the socio-technical, economic, and legal aspects of the metaverse. The study will draw upon a thematic analysis of semi-structured interviews with experts in the field of subject matter and compare their answers with the Chatbot responses provided by ChatGPT to extract the primary themes in the area of digital identity, virtual teamwork, data control, and cybersecurity. Although the two sources acknowledge the transformative potential of the metaverse in virtual business and immersive education, analysts note that the regulatory coherence, privacy regulations, and responsibility cutoffs must be established to eliminate the potential of systemic threats. The insights produced by AI, in their turn, underline the scalability and accessibility of metaverse technologies and tend to downplay the complexity of compliance and liability. The paper highlights the importance of legal uncertainty related to digital assets, decentralized governance, and AI-based interactions as the new space of insurance innovation and risk modelling. This study brings together technological vision and the professional judgment to fortify the existing knowledge on governance, liability and ethical accountability in the emergent digital economies, and provides policy implications to policymakers, insurers and legal professionals in the digital age.
Metaverse, Artificial Intelligence, Digital Governance, Risk Regulation, Insurance Law, Data Privacy, Cybersecurity.
Optimization of Electric Vehicles Charging Stations in Urban Areas: A Huff Model and Genetic Algorithm Study of Chandigarh, India
With the rapid adoption of electric vehicles (EVs) in India, a coherent approach in developing public charging infrastructure is essential. In this paper, we propose a hybrid framework integrating the Huff Model with a Genetic Algorithm (GA) to determine the optimal locations of EV charging stations in Chandigarh city. Chandigarh is the best choice for study because of its highest charger-to-vehicle ratio in the country. The Huff Model approximates the probability of individual demand nodes (residential and commercial) to select specific charging facilities in view of site attractiveness and location decay. The GA optimization process is iterative in search of the most efficient station configuration with the aim of maximizing aggregated accessibility and minimizing energy deficiency given the constraints of solar energy and battery storage based on Chandigarh 500 kWp photovoltaic (PV) chargers. A synthetic demand of 10 destinations in Chandigarh is used to simulate the demand of urban traffic with the load variation, solar production, and grid reliance. Computational evidence shows that the convergence of the GA fitness score between 0.45 and 0.95 with respect to multiple generations, indicating a satisfactory equilibrium in charging demand versus solar energy utilisation. With the inclusion of the PV capacity and battery state of charge in the algorithm we can reduce grid dependence by about 28 per cent compared to non-solar optimization runs. The resultant spatial distribution outlines the strategically ideal areas within the Sector 17, Sector 35, and the Industrial Phase I and the IT Park in respect to technical efficiency and enhanced consumer experience with a provision of accessibility, shorter wait time and greater dependability of the charging services delivery. This is the first study to offer a hybrid and scalable model of an EV charging infrastructure. In future, testing the model with real-time data can be insightful to develop the sustainable charging infrastructure across diverse cultures.
The Moderating and Mediating Roles of Parichay, Tatkal Parichay and Karuna-Bhaav in Online Impulsive Buying Behaviour: An Indian Perspective
The rapid growth of digital connectivity in India has caused a change in how people shop online. As mobile commerce and social media become increasingly similar, impulsive purchasing behaviour (IBB) has become a major force in Indian e-retail, making up a large part of all online purchases (Statista, 2023; IAMAI, 2023). This is not just because of persuasive algorithms or visual merchandising; it's also because of strongly held social and cultural norms that affect how people connect with each other in collectivist cultures like India (Pandey & Singh, 2020; Sinha & Tripathi, 1994). The Stimulus-Organism-Response (SOR) framework remains prevalent for interpreting how people make online decisions. While existing models fail to account how indigenous cultural constructs can affect mental states and behaviour (Mehrabian & Russell, 1974; Chan et al., 2017). This study looks at three novel Indian relational constructs — Parichay (deep-rooted interpersonal familiarity), Tatkal Parichay (rapid digital bonding), and Karuna-Bhaav (moral-emotional reciprocity)—to evaluate their roles as mediators and moderators in the SOR paradigm when it comes to online impulsive buying behaviour. Employing mixed-methods approach to survey 850 Indian internet buyers from both urban and rural areas, supplemented by 30 semi-structured interviews to learn about the cultural factors that affect digital transactions. Additionally, Structural Equation Modelling (SEM) jointly with bootstrapping was utilised to check the mediating and moderating effects of the constructs on the data. Structural Equation Modelling (SEM) is a multivariate statistical technique that analyses complex relationships among observed and latent variables through a combination of factor analysis and multiple regression. The results show that Tatkal Parichay (rapid digital bonding) has a big effect on the link between online stimuli (like influencer interaction, narrative, and real-time chat) and consumer arousal. On the other hand, Karuna-Bhaav (moral-emotional reciprocity) works as a strong link between emotional states and impulse buying intentions. For example, Tatkal Parichay intensifies consumers’ immediate reactions to external triggers, while Karuna-Bhaav transforms these emotional responses into purchasing behaviour. Furthermore, Parichay builds trust and long-term engagement in certain merchants or platforms over time. Observations indicate that Indian consumers respond to marketing stimuli not entirely on cognitive and emotional grounds, but also through relational and moral lens. The ramifications are substantial for marketers, platform designers, and policymakers aiming to create ethical, culturally attuned, and inclusive digital marketplaces. This study bridges the gaps between global behavioural models and everyday realities of Indian consumers by looking at consumer relationships through culturally sensitive point of view.
impulsive buying behaviour, Indian consumer culture, Parichay, Tatkal Parichay, Karuna-Bhaav, SOR framework, social commerce, digital trust, live streaming, emotional reciprocity
Preventing Agricultural Practice of Stubble Burning Through Applications of Artificial Intelligence
Stubble burning, a common agricultural practice, is widespread across most of Asia and contributes significantly to severe air pollution. Our analysis aims to inform policy decision-makers about the relevance of artificial intelligence in controlling stubble burning by providing a comprehensive and structured review of the related streams of literature. From our balanced review of the related empirical literature, we infer that AI can help manage the effects of climate change by utilizing limited resources with less human intervention. In this study, Structural Topic Modelling (STM) and Sentiment Analysis tools have been employed to demonstrate how artificial intelligence can help reduce stubble burning and contribute to environmental conservation.
Artificial Intelligence, Agriculture, Structural Topic Modelling, Air Pollution, Sentiment Analysis
Exploring the Nexus of Digital Transformation and Sustainable Development: A Systematic Review with Bibliometric Evidence
In light of growing concern regarding the planet’s environmental future, it is important for companies to prioritize sustainable development in their digital transformation initiatives, driving growth that is both environmentally responsible and technologically advanced. This study aims to document and synthesize the trends in the field of ‘digital transformation and sustainable development over the period of time. By analysing 976 Scopus indexed documents, the review uncovers a rapidly growing body of literature. The literature is globally diverse, with notable contribution from both developing and developed countries. Citation analysis identified key authors and influential work that have played an important role in shaping this field. A lot of individuals in the field are directly affected by this study, for instance consultant, managers, and policymakers.
Digital transformation, Sustainability, Systematic literature review, Bibliometric analysis, Sustainable development
Corporate Frauds and Gimmicks in Insurance and Financial Services Industry: The Recovering Perspective
The external environment has a direct impact on the financial services and insurance sectors. As a result, they are constantly under pressure to keep their financial figures stable. The end result is gimmicks and frauds! These pose the biggest risks to all parties involved. International markets are currently recovering from the 2008 global financial crisis. It is crucial to take into account the effects of these threats and safeguard the entire world community in order to improve management in recovering markets. This paper's goal is to examine the insurance and financial services industries for frauds and gimmicks that are common there, as well as the causes and origins of issues that lead to prudent management's downfall. The results are expected to assist investors with their investments and regulators in establishing stricter regulations and outlining the proper protocols for.
Enhancing Security and Transparency in Management Information Systems through Agent-Based Solutions
Management Information Systems (MIS) are essential to support organizations in data-driven decision-making. With increasing complexity of data, multi-user access, and integration with external platforms, MIS face adversities inflicted upon them by security, privacy, and transparency. In this paper, we propose an agent-based architecture for enhancing activities within the MIS from security and transparency perspectives. Intelligent software agents monitor, authenticate, and verify data transactions above the level of the organization independently, guaranteeing that every action becomes subjected to tracing and is compliant with the policies of the organization. The proposed architecture involves coordination by ill-disposed and immutable logging inspired by blockchain, and rule-based detection of anomalies, to provide adaptive protection against internal threats and unauthorized modifications. Experimental simulation results show an overall enhancement of 37% in reducing unauthorized access to data events and an improvement of 46% in audit transparency in comparison with traditional MIS security frameworks.
Management Information Systems, Agent-Based Systems, Security, Transparency, Blockchain, Rule-Based Monitoring, Multi-Agent Coordination.
Neuro-Symbolic Integration Using Knowledge Attention Graphs with Advanced Deep Learning Techniques for Detecting Brain Disorders
Recent advances in artificial intelligence have demonstrated remarkable progress in medical image analysis; however, most deep learning models remain black-box in nature and lack interpretability—an essential requirement in clinical decision support systems. This study proposes a Neuro-Symbolic Integration framework based on Knowledge Attention Graphs (KAGs) combined with advanced deep neural architectures for accurate and explainable detection of brain disorders. The framework integrates multimodal neuroimaging data—including MRI, EEG, and patient metadata—through a hybrid CNN–LSTM–Transformer embedding model that captures both spatial and temporal brain patterns. Extracted embeddings are then mapped to a Knowledge Attention Graph, where graph neural networks (GNNs) perform relational reasoning guided by domain-specific neuro-symbolic rules. A symbolic reasoning layer ensures consistency with established medical knowledge, enhancing interpretability and transparency. Experimental evaluation on benchmark datasets (ADNI, OASIS-3, and TUH EEG) demonstrates that the proposed NS-KAG model achieves a classification accuracy of 96.87%, an AUC of 0.98, and an Explainability Index (EI) of 82.4%, outperforming conventional CNN, Transformer, and GNN-based approaches. Statistical analysis (p < 0.01) validates the significance of performance improvements. The integration of symbolic reasoning with neural attention not only improves diagnostic accuracy but also bridges the gap between data-driven learning and human-understandable reasoning, thereby enabling trustworthy and transparent AI-driven neurodiagnostic systems.
Neuro-symbolic AI, Knowledge Attention Graph (KAG), Explainable deep learning; Graph neural networks (GNN), Brain Disorder Detection, Explainable AI, Multimodal neuroimaging; Convolutional neural networks (CNN), Long Short-Term Memory (LSTM); Transformer architecture; Symbolic reasoning; Brain disorder detection; Alzheimer’s disease.
Service - Dominant Logic & Stakeholder Theory in Higher Education Branding: A Conceptual Framework for Brand Identity Co-Creation
This study examines the role of multiple stakeholders in the co-creation of brand identity in private, unaided higher education institutions (HEIs) in India. Drawing on the Service-Dominant (S-D) logic of marketing and Stakeholder Theory, it addresses a gap in understanding how diverse actors collaboratively shape institutional brand identity. A qualitative research design was adopted, involving 70 semi-structured interviews with students, faculty, staff, alumni, parents, industry recruiters, and admission consultants from three private HEIs in India. The thematic analysis of qualitative data was performed to identify recurring patterns and processes in brand identity co-creation. The results highlight three interconnected processes—stakeholder feedback, collaborative engagement, and institutional adaptation—that collectively shape HEI brand identity. These processes operate through ongoing feedback loops, multi-stakeholder partnerships, and iterative refinements to institutional offerings and positioning. A conceptual framework for co-creating brand identity is developed, integrating value co-creation principles with stakeholder engagement. The findings provide actionable guidance for HEI leaders to strengthen stakeholder engagement, empower students and faculty as active brand agents, enhance alumni and industry collaborations, and align messaging through strategic partnerships with admission consultants. This research advances higher education branding literature by integrating S-D logic and stakeholder theory, offering a dynamic framework for understanding brand identity as a co-created outcome.
Perceived Organizational Support as a Mediator: Mitigating the Impact of Perceived Work Stress on Work Meaningfulness
To find out the link and relationships between perceived work stress (PWS), perceived organizational support (POS), and work meaningfulness (WM) among banking sector workers is the subject of this research study. A sample of 500 public and private sector Bank employees, were taken from Punjab, India. Regression mediation analysis was carried out to study the direct and indirect effects of PWS on WM, using POS as a mediator. The results show that PWS both directly and indirectly affected the WM, with POS partially mediating this relationship. Lower level of the PWS is related with the increased levels of WM, directly and indirectly through increased level of POS. The findings suggest that organizations can enhance their employee’s satisfaction and wellbeing levels by giving a working environment which is supportive and addresses the effect of job stress on employees. The study stresses how important it is to consider the relationships between PWS, POS, and WM in addition to highlighting their interplay. For companies wanting to improve employee performance, the results have practical implications. Businesses may foster a good and effective environment that advantages both the employees and the organization as a whole by addressing the workplace stress and emphasizing the organizational support. Expanding the body of knowledge on PWS, POS, and WM, this study sets the basis for further investigation into the links between these elements.
perceived work stress, mediation analysis, organizational support, work meaningfulness.