Prin. L. N. Welingkar Institute of Management Development and Research

ABSTRACT This paper uses a systematic literature review (SLR) of 280 peer‐reviewed articles to evaluate the function of carbon footprint (CFP) management as a motivator for sustainability in industry. The study considers industry perspectives and acknowledges the growth of CFP research as a key measurement area for organizations as well as a strategic enabler to align industry with global climate targets. The study identifies relevant gaps, particularly the absence of comparative literature related to the effects of CFP non‐compliance relative to regulations, and a lack of longitudinal studies regarding the long‐term effects of CFP policies being initiated or abandoned. The study provides a linkage to actionable sustainable practices, as a guided structure through five stages of industrial production, and makes suggestions for actionable outcomes for researchers, industries, and policy, encouraging a more comparative approach to and integrated management of CFP in core industrial strategy to enhance sustainability benefits.

ABSTRACT This review investigates the transformation of the financial services sector through the implementation of artificial intelligence (AI). The study employed a TCCM framework based systematic literature review integrated with content analysis to extract meaningful insights from 96 articles published between 2010 and 2024. Drawing upon the emerging literature on AI‐enabled significant transformation in the financial industry, the study identifies four themes: (1) leveraging algorithms for enhanced business decision‐making and fraud detection, (2) predictive analytics in financial decision‐making, (3) AI‐driven robo‐advising and personalized financial services, and (4) challenges surrounding compliance and trust. We also trace the evolution of AI adoption in finance, highlighting a significant acceleration in implementation. While AI has transformed areas such as trading, credit scoring, and customer support, challenges remain in the areas of using AI ethically, data privacy, and regulatory compliance. Our findings reveal a noticeable absence of robust theoretical frameworks in existing research. To address these gaps, we propose future research directions, including exploring novel theories such as Prospect Theory, utilizing data governance frameworks to expand the geographical scope beyond major markets. This review provides a comprehensive analysis of AI's influence on the financial services sector and offers recommendations for future research in this rapidly evolving field.

Purpose : This study aimed to analyze the influence of bank performance on economic growth. We proposed a framework of banking variables to gain a holistic picture. Design/Methodology/Approach : To identify the causal relationship, we ran cointegration tests and vector autoregression (VAR). In addition, we used the impulse response function test. This helped in analyzing the response of GDP growth to banking shocks. Findings : We identified that banking variables like domestic credit (DC), return on equity, and capital adequacy ratio can stimulate economic growth. Long-term and short-term influence of banking performance on economic growth. Improved performance of banks can translate to economic growth. Our results also indicated that well-capitalized banking, high return on equity, and increased credit access were critical drivers of GDP growth. Practical Implications : This research offered important insights into the role of banking performance in influencing economic growth. We recommended that policymakers implement measures to improve capital adequacy, enhance profitability, and increase credit flow for sustained economic success and development. Originality/Value : This study added to existing research by examining how banking performance influenced economic growth in a developing context. Unlike prior studies that focused mainly on advanced economies, our analysis centered on India, offering evidence from an emerging financial system and providing context-specific insights.

The effective integration of blockchain technology and artificial intelligence (AI) has the potential to change healthcare management. Two cutting-edge developments in the healthcare industry are blockchain and artificial intelligence. Blockchain, an open network for information sharing and permission, is being used in e-Health to apply artificial intelligence models. Healthcare workers will be able to view patient medical records on the blockchain. Artificial intelligence (AI) makes use of a wide range of suggested algorithms, decision-making power, and vast amounts of data. Health care services can be made more decentralized, transparent, safe, and impenetrable with the use of blockchain technology. AI needs cryptographic records to be stored, and blockchain makes this possible. Applications of artificial intelligence in healthcare management include chatbots for diagnosis and treatment, predictive analytics, personalized medicine, and more. Blockchain technology has applications in supply chain management, clinical trial management, interoperability, and data security and integrity in healthcare management. A more effective, safe, and patient-centered healthcare ecosystem can be created by integrating blockchain technology and artificial intelligence (AI) into healthcare management. It can encourage the development of a wide range of applications in radiology, cancer treatment, cardiology, dermatology, and fundoscopy that may save patients' lives.

The music industry, has been transformed by the introduction of new startups due to the advent of the Artificial Intelligence (AI) where machine learning, deep learning, and natural language processing are used to redefine the music creation, production, and distribution. The paper will discuss the management practices which may be utilized in the success of AI-based music startups such as the organizational models, innovation models and the mechanisms of sustainable growth. Applications of AI to music have touched various themes such as generative music composition, machine learning-based playlist optimization, machine mastering, machine-generated music and emotion-based playlists, and audience analytics. These startups are difficult to administer as they will require a mediating zone between the invention of technology and aesthetic arts and be necessitated by inter-disciplinary management, which will incorporate the engineering precision and aesthetic sense. The strategic aspects found to be agile development cycles, ethical data governance, intellectual property management and collaboration with artists and technologists. The paper is devoted to the dynamic business strategies, such as the so-called AI-as-a-Service (AIaaS) and subscription-based models which can be scaled and made to maintain the relationships with customers. Similarly, strategic cooperation with record labels, streaming applications as well as independent artists are crucial agents of market entry. Acquiring talent strategies should give priority to hybrid skills with data science, good engineering and music theory to be able to maintain product relevance and continuity of innovation. Issues like data bias, ambiguity in copyright and creative ownership are resolved by transparent algorithm design and management practices which are stakeholder-centric. The conclusion of the paper is that effective AI-driven music startups are built on a dynamic leadership, cross-domain partnership, and constant ethical review of AI work. With creative innovation and sustainability of business, these startups can transform the entire music ecosystem across the world, enabling a more personalized, intelligent, and inclusive future of music creation and consumption.

Immune responses depend on the identification and prediction of peptides that bind to MHC (major histocompatibility complex) class I molecules, especially when it comes to the creation of vaccines, cancer immunotherapy, and autoimmune disorders. The ability to predict and evaluate MHC class immunoproteomics have completely transformed I epitopes in conjunction with immunoinformatics technologies. However, precisely identifying epitopes across various populations and situations is extremely difficult due to the complexity and diversity of MHC class I binding peptides. The most recent developments in immunoinformatics technology that have improved MHC class I epitope prediction are examined in this article. The sensitivity and specificity of epitope prediction have been greatly enhanced by recent developments that have concentrated on bioinformatics algorithms, artificial intelligence, and machine learning models. Potential epitopes are predicted using large-scale peptide-MHC binding data, structural characteristics, and interaction dynamics using tools like NetMHC, IEDB, and MHCflurry. Additionally, the integration of proteomic, transcriptomic, and genomic data has improved prediction accuracy in real-world scenarios by enabling more accurate identification of naturally occurring peptides. Furthermore, newer techniques like deep learning and multi-omics data integration have the potential to overcome peptide binding prediction constraints. Utilizing these technologies is expected to speed up the identification of new epitopes, improve the accuracy of immunotherapy techniques, and enable customized vaccine development. These innovative techniques, their uses, and potential future developments for improving MHC class I epitope prediction in immunoproteomics are highlighted in this study.

Frugal innovation considers societal and operational challenges as starting points for innovative solutions that could serve as drivers of sustainable development. Prior research, however, has done little to understand firm level requirements to set-up a frugal innovation-based business. In this article, we established a link between frugal innovation and the sustainable business model concept. More specifically, the research questions are: 1) How do societal and operational pressures together influence business activities and 2) how can these pressures be assessed analytically? We present an analytical framework that can be used in assessing frugal innovations for long-term sustainability and we demonstrate the functioning of the framework with the empirical analysis of three case firms.

The paper tries to analyze a study on how AI-powered robo-advisers are changing the face of banking in the modern world with the potential democratization of financial services by giving more personalized, efficient, and cost-effective solutions. Based on predictive analytics and machine learning, this study will look at some important factors that can influence customer readiness, adoption, and performance of robo-advisors. Respondents in this survey are very diverse and are administered with the help of a structured questionnaire. The data is then analyzed using advanced techniques, including decision trees, clustering, and regression. The key findings underline the main drivers of adoption as digital literacy, trust in AI, cost, and personalization, while the main barriers identified include privacy concerns, lack of trust, and usability challenges. Hierarchical clustering provided insights that clear customer groups with different concerns could be distinguished, rendering banks actionable strategies to address these barriers. This research has shown the improvement in customer education, building of trust, and cost-effectiveness as the main accelerators to increase the adoption of robo-advisers. These findings add to the burgeoning literature on AI in banking and inform future research directions.