Mr. Nitin Janki Mamatu Pimparkar, Dr. Ramraj T. Nadar
Page 01 - 16
Abstract
The
rapid digitisation of financial services has fundamentally transformed the
operational framework of banking institutions in India. Urban Cooperative Banks
(UCBs), which serve as vital grassroots financial institutions, play a crucial
role in advancing financial inclusion by extending banking services to small
businesses, middle-income groups, and underserved communities across urban and
semi-urban regions. However, the accelerating adoption of digital banking
platforms, artificial intelligence (AI), cloud computing, and data analytics
has resulted in the extensive collection and processing of personal and
financial information, thereby raising critical concerns related to data
privacy, cybersecurity, and ethical governance. The Digital Personal Data
Protection (DPDP) Act, 2023, enacted by the Government of India, introduces a
comprehensive legal framework for safeguarding digital personal data by
defining the obligations of data fiduciaries, establishing rights for data
principals, and mandating accountability mechanisms for organisations
processing personal data (Government of India, 2023). For UCBs, compliance with
the DPDP Act is not merely a regulatory necessity but also a strategic
opportunity to enhance customer trust, strengthen cybersecurity practices,
improve governance standards, and enable the responsible adoption of AI-driven
banking solutions.
This
research paper examines the readiness of Urban Cooperative Banks to implement
the requirements of the DPDP Act, 2023, particularly in the context of
AI-enabled banking operations. The study adopts a qualitative exploratory
approach based on secondary data collected from regulatory reports, academic
literature, government publications, Reserve Bank of India (RBI) guidelines,
and industry studies. The analysis evaluates UCB preparedness across five key
dimensions: legal compliance, governance capability, AI adoption, cybersecurity
readiness, and ethical responsibility. The study identifies that while UCBs
have made notable progress in digital transformation, significant challenges
remain, including limited technological infrastructure, high compliance costs,
a shortage of skilled professionals, dependence on legacy systems, and
inadequate AI governance mechanisms. To address these issues, the paper
proposes a DPDP compliance framework incorporating privacy-by-design
principles, AI governance practices, cybersecurity enhancement, employee
training, vendor management, and regulatory oversight. The research concludes
that effective implementation of the DPDP Act, 2023 can enable Urban
Cooperative Banks to achieve responsible digital transformation while protecting
customer privacy, strengthening institutional resilience, and enhancing trust
in India’s cooperative banking ecosystem.
Keywords: Data
Privacy, DPDP Act 2023, Urban Cooperative Banks, Artificial Intelligence, AI
Governance, Banking Ethics, Cybersecurity, Data Protection, Digital Banking,
Financial Inclusion.
Cite as
Mr. Nitin Janki Mamatu Pimparkar, & Dr. Ramraj T. Nadar. (2026). Data Privacy in Grassroots Banking – Assessing DPDP Act Readiness in Urban Cooperative Banks in the Era of AI. International Journal of Arts, Social Sciences and Humanities, 04(04), 01–16. https://doi.org/10.5281/zenodo.23011745
Dr. Anita Rane- Kothare, Ms. Mala Topandas Khanchandani
Page 17 - 26
Abstract:
Artificial Intelligence (AI) has revolutionized the education sector with new teaching-learning models, customized learning spaces and enhanced skill development opportunities. Generative AI technologies such as ChatGPT, Gemini, and AI-assisted educational platforms are increasingly being adopted by students, teachers, and educational institutions. At the same time, the focus on the implementation of the National Education Policy (NEP) 2020 has been on technology integration, multidisciplinary learning, experiential learning, and development of digital skills. Additionally, the use of AI-powered interactive exhibits, virtual learning spaces, and digital heritage preservation methods for educational goals has transformed museum practices.
The present study explores the significance of AI in the field of education and skill development, particularly in the context of generative AI, the NEP 2020 and current museum practices in the Mumbai Metropolitan Region. The study aims to gain insights into students’ and teachers’ awareness along with the effectiveness and challenges of using AI tools in education, as well as the awareness and challenges of using AI tools in museum work. A structured questionnaire was used to gather data from 200 respondents from the population, with stratified random sampling. Students (60%), teachers (25%) and museum professionals (15%) were among the respondents.
The results suggest that G.A.I. has made a huge contribution to the learning efficiency, research, creativity and skill acquisition of learners. The study also shows that the goals of NEP 2020 are positively facilitated with AI driven education practices. AI-enhanced digital exhibits were found to engage and enhance learning for visitors, according to museum professionals.
The research concludes that AI is a game-changer in the education and cultural learning landscape. To leverage the potential of AI in education and museum-based learning effectively, implementation strategies, digital literacy programs, and ethical guidelines are crucial. The research provides valuable guidance for policymakers, educators, museum administrators, and researchers in developing innovative, technology-enhanced learning environments that enrich educational experiences and promote greater engagement.
Keywords: Artificial Intelligence in Education, Generative AI, NEP 2020, Museum Innovation and Skill Development.
Cite as
Dr. Anita Rane- Kothare, & Ms. Mala Topandas Khanchandani. (2026). A Study of AI in Education and Skill Development: Exploring Generative AI, NEP 2020, and Modern Museum Practices in MMR. International Journal of Arts, Social Sciences and Humanities, 04(04), 17–26. https://doi.org/10.5281/zenodo.23018463
CA Pravin Pawar, CA Dr. Vijay Satra, CS Gopalkrishna Chodnekar
Page 27 - 35
Abstract
Financial inclusion has become a key part of economic development and is essential for providing easy and low-cost access to financial services to individuals and businesses. From machine learning to AI-powered predictive analytics, AI-driven tools like robo-advisory platforms, biometric authentication, and chatbots have improved financial institutions’ access to previously unreached groups. The use of digital payment systems, mobile banking apps, and AI-powered financial platforms has helped raise awareness and participation in the formal financial system, particularly in urban areas like Dadar, Mumbai. The present study aims to explore the position of AI in financial inclusion for promoting sustainable advancement of FinTech in Dadar. This study aims to shed light on how AI technologies enable financial accessibility, overcome operational challenges, enhance customer engagement, and drive sustainable financial growth. Trends and developments in the financial sector are analyzed using secondary information from government reports, publications of Reserve Bank India, industry reports on FinTech, research articles and statistical databases. The results indicate that AI-driven financial services have incremented the efficiency of transactions, cut the cost of services, strengthened the fraud prevention process, and broadened access to banking services for a variety of user groups. Moreover, the sustainable development of FinTech has also led to economic empowerment, financial literacy, and financial resilience. The study concludes that AI-driven financial inclusion is a critical element in driving sustainable FinTech growth in Dadar, and holds tremendous potential to enhance the financial ecosystem by providing innovation, accessibility, and responsible technological advancements.
Keywords: Artificial Intelligence (AI), Financial Inclusion, FinTech Development, Digital Financial Services and Sustainable Finance.
Cite as
CA Pravin Pawar, CA Dr. Vijay Satra, & CS Gopalkrishna Chodnekar. (2026). A Study of AI-Driven Financial Inclusion and Sustainable Fintech Development in Dadar. International Journal of Arts, Social Sciences and Humanities, 04(04), 27–35. https://doi.org/10.5281/zenodo.23020054
Mr. Pradeep Ramashankar Mali, Dr. Savita Nagar
Page 36 - 47
Abstract
The rapid expansion of cloud kitchens in urban India has reshaped the food service landscape, yet the mechanisms through which food quality translates into consumer purchase intention remain insufficiently understood. This study examines the influence of food quality on consumer intention towards cloud kitchens in Mumbai, with parallel mediating roles assigned to perceived value and trust. Grounded in stimulus–organism–response (S–O–R) theory and signalling theory, a cross-sectional quantitative design was adopted. Data were collected from 400 respondents who had ordered food from cloud kitchens in Mumbai during the preceding six months, using a structured questionnaire measured on a five-point Likert scale. Convenience sampling was employed, and data were analysed using the General Linear Model with parallel mediation in Jamovi. The results indicate that food quality significantly enhanced both perceived value (β = 0.698, p < .001) and trust (β = 0.824, p < .001). Perceived value (β = 0.379, p < .001) and trust (β = 0.476, p < .001) in turn positively influenced consumer intention. The direct effect of food quality on consumer intention was not significant (β = 0.087, p = .059), while both indirect paths were significant, with trust exerting the stronger mediating effect (β = 0.392) than perceived value (β = 0.265). The model explained 75.3% of the variance in consumer intention. The findings establish full parallel mediation, suggesting that cloud kitchen operators should prioritise food quality as a signal that builds trust and value, which subsequently drive repeat patronage.
Keywords: cloud kitchens; food quality; perceived value; trust; consumer intention; parallel mediation; Mumbai.
Cite as
Mr. Pradeep Ramashankar Mali, & Dr. Savita Nagar. (2026). The Influence of Food Quality on Consumer Intention Towards Cloud Kitchens in Mumbai: The Parallel Mediating Roles of Perceived Value and Trust. International Journal of Arts, Social Sciences and Humanities, 04(04), 36–47. https://doi.org/10.5281/zenodo.23038758
Dr. Nitin Dwivedi
Page 48 - 54
This study investigates the influence of
artificial intelligence (AI) awareness, AI adoption, and perceived benefits of
AI on the economic empowerment of women entrepreneurs. A structured
questionnaire was administered to 280 women entrepreneurs across diverse
business sectors, and data were analyzed using multiple linear regression. The
regression model explained 59% of the variance in economic empowerment (R² =
0.59, F(3, 276) = 132, p < .001). AI adoption (β = 0.345, p < .001) and
perceived benefits of AI (β = 0.401, p < .001) emerged as significant
predictors, while AI awareness did not reach statistical significance (β =
0.098, p = .095). The findings underscore the importance of moving beyond
awareness to actual adoption and positive perception of AI as critical pathways
for enhancing women’s economic empowerment. Implications for policymakers and
support organizations are discussed.
Keywords: AI awareness; AI adoption; perceived
benefits; economic empowerment; women entrepreneurs; digital entrepreneurship
Cite as
Dr. Nitin Dwivedi. (2026). Artificial Intelligence and Economic Empowerment of Women Entrepreneurs: The Role of AI Awareness, AI Adoption, and Perceived Benefit. International Journal of Arts, Social Sciences and Humanities, 04(04), 48–54. https://doi.org/10.5281/zenodo.23040705
Ms. Sonia Lal, Ms. Snehal Pandey
Page 55 - 64
Abstract
The rapid diffusion of Artificial Intelligence (AI) tools into higher education has introduced a new axis of inequality that extends beyond the conventional “digital divide.” While earlier scholarship focused on disparities in internet connectivity and device ownership, this study argues that access alone no longer captures the full picture of educational inequality in the AI era. It proposes the concept of an “AI Divide” — a multidimensional gap encompassing who has access to AI tools, who possesses the digital literacy to use them effectively, who derives genuine academic benefit, and how these disparities generate new forms of exclusion.
Situated within Indian higher education — a context marked by significant socioeconomic, regional, and institutional diversity — the study draws on a randomly sampled cohort of 111 students to examine AI tool adoption, competence, and perceived academic impact. It investigates the factors shaping unequal access, disparities in skills required for effective academic use, variation in reported academic benefit, and the mechanisms through which AI adoption creates or reinforces existing educational inequalities.
Drawing on the sociology of education and digital-inequality literature — including cultural capital, the “second-level digital divide,” and algorithmic literacy — this research moves the discourse from a binary understanding of access to a nuanced analysis of usage, skill, and outcome disparities, contributing to policy discussions on equitable AI integration in the Global South.
Keywords: Artificial Intelligence (AI), AI Divide, Digital Inequality, Digital Literacy, Educational Inequality, Cultural Capital.
Cite as
Ms Sonia Lal, & Ms Snehal Pandey. (2026). From Digital Divide to AI Divide: A Sociological Study of Educational Inequality in Indian Higher Education. International Journal of Arts, Social Sciences and Humanities, 04(04). https://doi.org/10.5281/zenodo.23055691
Dr. Sachin Chandrakant Pimple, and CA (Dr.) Kishore Peshori
Page 65 - 74
Abstract
This study examines whether financial literacy is associated with environmental, social, and governance (ESG) awareness and self-reported participation in ESG-themed investments among retail investors in Mumbai.
A structured questionnaire was administered to 224 individual investors with an active demat or trading account and at least one year of investing experience during FY 2025-26. Financial literacy and ESG awareness were measured on 0-20 scales. The analysis used descriptive statistics, correlations, Chi-square tests, t-tests, ANOVA, and supplementary binary logistic regression. Scale reliability was satisfactory (alpha = 0.84 and 0.81, respectively).
Financial literacy was strongly associated with ESG awareness (r = 0.62, p < 0.001) but more modestly with ESG participation (r_pb = 0.28, p < 0.001). Financial-literacy category and participation were associated, chi-square(2, N = 224) = 15.64, p < 0.001, Cramer’s V = 0.26. In the adjusted model, ESG awareness (OR = 1.23, p < 0.001), financial literacy (OR = 1.15, p = 0.005), and income band (OR = 1.36, p = 0.027) were statistically significant; age and gender were not. Among highly ESG-aware respondents, 43.0 per cent reported no ESG holding.
The study links conventional financial literacy with ESG-specific knowledge and behaviour in an emerging-market retail-investor setting. It identifies an awareness-to-action gap and supports clearer ESG disclosures and evidence-based investor education. Because the design is cross-sectional and uses convenience sampling, the findings indicate association, not causation.
Cite as
Dr. Sachin Chandrakant Pimple, & CA (Dr.) Kishore Peshori. (2026). Financial Literacy, ESG Awareness, and Sustainable Investment Participation: Evidence from Individual Investors in Mumbai. International Journal of Arts, Social Sciences and Humanities, 04(04), 65–74. https://doi.org/10.5281/zenodo.23057878
Alwyn Alfred Carvalho, Bhumika Gorakhnath Salvi
Page 75 - 82
Abstract
English Literature departments face a peculiar version of cheating using Artificial Intelligence, particularly Large Language Models (LLMs). Most of the canon these courses teach from is already public domain, already digitized, and already sitting inside the training data of most major language models. A student does not need to ask an AI to think about Hamlet; the model has, in some sense, already read it a thousand times over. This paper looks at what actions departments may take for it, and where those responses tend to fall apart. Timed online examinations have picked up a set of technical countermeasures over the past year or so: hidden prompt injections buried in the question text, watermarked or visually distorted question images, AI-based proctoring. One documented case from July 2026 caught thirty-two of thirty-five students this way. But these tricks have a short shelf life. Students figure them out quickly, model providers patch around them faster, and proctoring software frequently incorrectly flag attempts which may be genuine. Take-home writing is a harder problem, and probably the more important one for a literature department, since AI detectors themselves are unreliable and biased against certain kinds of writers. Our argument, briefly, is that assignments need to stop rewarding a finished essay and start rewarding the visible work behind it including drafts, in-class discussion tied directly into the prompt, or a short oral defence of the argument. None of this is a permanent fix. It is closer to a set of habits that make AI-generated submissions harder to pass off as one’s own, drawn from work in AI security, assessment design, and literary pedagogy that rarely gets read together.
Keywords: Academic integrity, Generative AI in education, Assessment design, Prompt injection, English literature pedagogy
Cite as
Alwyn Alfred Carvalho, & Bhumika Gorakhnath Salvi. (2026). Beyond the Prompt: A Dual-Layer Framework for AI-Resistant Assessment in English Literature Education. International Journal of Arts, Social Sciences and Humanities, 04(04), 75–82. https://doi.org/10.5281/zenodo.23059926
Ms. Susan Maria. Abraham, Dr. Parag Ajagaonkar
Page 83 - 89
Abstract
The fast-growing information technology (IT) industry has offered new employment opportunities to women and new work structures that are demanding and involve long working hours, tight deadlines, global teams, continuous connectivity and high performance expectations. These demands are often compounded for women in the IT profession, especially when married and having children and caring responsibilities. Workplace flexibility, as a tool, has become a significant option to deal with competing work and life demands in this context. To address these issues, the paper investigates two aspects of flexibility – task flexibility and role flexibility – and how they can aid in work–life balance for women working in the IT sector. Task flexibility is the degree of freedom and discretion that workers have in relation to how, when and, in some instances, where work tasks are executed. Role flexibility is the ability to adapt/modify, redistribute or temporarily change professional role due to personal/organisational situations. The paper suggests that these forms of flexibility can help to alleviate work–family conflict, increase autonomy, increase job satisfaction, and support women’s retention and career continuity in the IT sector. But being flexible doesn’t always yield positive results. However, if expectations of availability are too high, if there are career disadvantages for having a flexible working schedule, or if the culture of an organisation promotes long hours, the benefits of EWP can be compromised.
The paper therefore suggests that it is important for the organisations to implement structured gendered flexible policies with inclusive leadership, the implementation of performance-based evaluation and clear work-life boundaries.
Keywords: Task flexibility, role flexibility, Women IT professionals, Work–life balance, Flexible working, Role conflict, Workplace flexibility, Information technology
Cite as
Ms. Susan Maria. Abraham, & Dr. Parag Ajagaonkar. (2026). Task and Role Flexibility as Enablers of Work–Life Balance among Women Information Technology Professionals. International Journal of Arts, Social Sciences and Humanities, 04(04), 83–89. https://doi.org/10.5281/zenodo.23064225
Ms. Bhoomika Jain, Mrs. Harpreet Kaur
Page 90 - 97
Abstract: Artificial Intelligence (AI) is increasingly becoming part of teaching, learning, assessment and skill-development activities. AI tools can support personalized learning, provide rapid feedback, assist with problem solving and help students practise technical and communication skills. At the same time, excessive or uncritical use of AI may create risks such as over-dependence, reduced independent thinking, inaccurate information, privacy concerns and academic-integrity problems. This research paper examines the relationship between AI use in education and student skill development, with particular attention to AI literacy, conceptual understanding, critical thinking, problem solving, communication and responsible use of AI. The study follows a secondary-data research design using a publicly available Kaggle dataset on academic outcomes and AI dependency, supported by findings from recent peer-reviewed research. The proposed analysis uses descriptive statistics, an independent-samples t-test, correlation analysis and regression-based interpretation. The paper argues that the educational value of AI depends less on simple frequency of use and more on purposeful, ethical and guided use that strengthens rather than replaces human skills.
Keywords: Artificial Intelligence, AI in Education, AI Literacy, Skill Development, Critical Thinking, Academic Performance, AI Dependency, Higher Education.
Cite as
Ms. Bhoomika Jain, & Mrs. Harpreet Kaur. (2026). An Analysis of AI in Education and Skill Development. International Journal of Arts, Social Sciences and Humanities, 04(04), 90–97. https://doi.org/10.5281/zenodo.23065807
Dr CA Krishnan Jaikumar
Page 98 - 106
Abstract
The increasing complexity and dynamism of global supply chains have rendered traditional inventory management approaches insufficient for meeting contemporary operational demands. Artificial intelligence (AI) offers transformative capabilities for inventory optimization through enhanced demand forecasting, automated replenishment, cost reduction, and supply chain resilience. This paper examines the integration of AI technologies in inventory management and their impact on operational efficiency through a systematic literature review and analysis of real-world case studies. The findings reveal that machine learning and deep learning models substantially outperform traditional statistical methods in demand prediction accuracy, particularly for intermittent and volatile demand patterns. AI-driven automated replenishment systems reduce stockouts and excess inventory, while predictive analytics enable proactive risk mitigation. Case analyses of Amazon, Walmart, and Zara demonstrate that organizations leveraging AI achieve measurable improvements in inventory turnover, waste reduction, and supply chain responsiveness. The paper concludes with a discussion of implementation challenges and future research directions, including the potential of generative AI and digital twin technologies.
Keywords: Artificial intelligence, inventory management, demand forecasting, supply chain resilience, operational efficiency, machine learning
Cite as
Dr CA Krishnan Jaikumar. (2026). AI-Based Inventory Management and Operational Efficiency. International Journal of Arts, Social Sciences and Humanities, 04(04), 98–106. https://doi.org/10.5281/zenodo.23077668
Komal Ravindra Pawar
Page 107 - 116
Not Available
Cite as
Komal Ravindra Pawar. (2026). Understanding the Role of Artificial Intelligence in Preventing Digital Payment Fraud: A Study of Consumer Awareness, Trust and Perceived Security in Mumbai Suburban Area. International Journal of Arts, Social Sciences and Humanities, 04(04), 107–116. https://doi.org/10.5281/zenodo.23078386
Dr. (CA) Nishesh S Vilekar, Ms. Manmeet Kaur
Page 117 - 123
Not Available
Cite as
Dr. (CA) Nishesh S Vilekar, & Ms. Manmeet Kaur. (2026). An Empirical Study on Awareness and Adoption of AI-Based Accounting Software Among Accountants. International Journal of Arts, Social Sciences and Humanities, 04(04), 117–123. https://doi.org/10.5281/zenodo.23079008
Ms. Khyatee Dilip Lakhani
Page 124 - 130
Not Available
Cite as
Ms. Khyatee Dilip Lakhani. (2026). NEP 2020 and Artificial Intelligence: Transforming Teaching, Learning, and Assessment in Higher Education. International Journal of Arts, Social Sciences and Humanities, 04(04), 124–130. https://doi.org/10.5281/zenodo.23081393
Dr. Sadhana Chhatlani
Page 131 - 138
Abstract
It is evident that Artificial Intelligence (AI) now plays and important role in every students life. It is being adapted progressively becoming an inevitable part of almost every student’s academic performance. The use of AI tools such as ChatGPT, Gemini, Canva, and many more AI-based learning platforms support student learning effectively. These tools aid as search engine machines used to extract information. Students often use AI learning based platforms for synthezising challenging concepts, seeking creative assignment ideas. They also use AI tools for taming writing skills and specifically during preparation of their exam preparation. Generation Z, is known to be digitally native so they have grown up in an environment which is highly digital in nature. So they are highly privileged to secure an environment that not only supports but also openly accepts wide usage of AI based technologies. This adoption of AI based technology learning not only designs their day to day living easy, but also supports them in their learning processes. AI tools facilitate learning quicker in less time and features personalised learning. AI based learning tools are easily accessible for students as they are either free of cost or offered to students at lower costs. Although excessive dependence on AI based tools for learning is not fit for students as, it restricts their independent critical thinking blocking their creativity minds and hinders problem-solving abilities. The main purpose of the research paper was to scrutinize the impact of AI tools on the learning behaviour of Gen Z students. The study is an attempt to focus on gauging the frequency with which the Gen Z students use AI learning tools, understanding the purpose of AI usage. An analysis is done through to pivot lenses on the influence of AI based learning on various parameters like learning efficiency, change academic engagement, and the perception of students. It will give a clear picture whether students hold AI based learning as an advantage or a possible limitation when adopted for educational purposes.
Keywords: Artificial Intelligence, AI Tools, Generation Z, Learning Behaviour, Students, Academic Engagement, Technology in Education
Cite as
Dr. Sadhana Chhatlani. (2026). Impact of Artificial Intelligence Tools on the Learning Behaviour and Academic Engagement of Generation Z Students. International Journal of Arts, Social Sciences and Humanities, 04(04), 131–138. https://doi.org/10.5281/zenodo.23115874
Nisar Saiyed and Sneha Singh
Page 139 - 146
Abstract
Background: As the reliance on AI keeps growing, it becomes important to understand its psychological implications. The study contributes to the growing literature on AI and mental health by investigating the predictive quality of AI dependency and distress tolerance in the use of avoidance coping in response to stressful situations.
Aims: The present research aims to examine the predictive relationship between AI dependency, distress intolerance and avoidance coping among young adults in Mumbai.
Method: 90 participants from Mumbai (60 Females, 29 Males) who were aged between 18-25 years (M=20.8, SD=2.38) and used AI apps like ChatGPT and Gemini, were selected using convenience sampling. All participants completed the Generative AI Dependency Scale (Goh et al., 2025), Distress Tolerance Scale (Simons & Gaher, 2005) and Ways of Coping Scale-Revised (Folkman & Lazarus, 1985). Correlation and simple linear regression was computed for statistical analysis.
Results: Findings of the research indicate that AI dependency showed a significant positive correlation with avoidance coping (p < .01) and hence was a statistically significant predictor. Furthermore, distress intolerance showed a weak correlation with avoidance coping and was therefore not a statistically significant predictor (p > .05) of avoidance coping.
Conclusion: The findings of this study highlight the predictive role of AI dependency in the use of avoidance coping when faced with stressful situations. The role of distress tolerance in avoidance coping needs to be studied further in a more diverse and larger population. The findings may help develop strategies that promote healthy coping skills in stressful situations in the era of increasing AI use.
Keywords: AI Dependency, Distress Intolerance, Avoidance Coping, Young Adults
Cite as
Nisar Saiyed, & Sneha Singh. (2026). Beyond AI Use: Examining The Predictive Quality of AI Dependency & Distress Intolerance in Avoidance Coping Among Young Adults in Mumbai. International Journal of Arts, Social Sciences and Humanities, 04(04), 139–146. https://doi.org/10.5281/zenodo.23116714
Mandeep Singh Ahuja, and Dr. Devendra Lodha
Page 147 - 156
Abstract:
Introduction
The rapid diffusion of financial technology (fintech) has fundamentally reshaped the delivery of retail banking services across urban India, with Mumbai the country’s financial capital serving as an early adopter of digital banking innovation. Mobile banking applications, the Unified Payments Interface (UPI), robo-advisory tools, artificial-intelligence-driven chatbots and contactless payment systems have altered the manner in which customers interact with banks, raising important questions about how these technologies shape perceived service quality and, in turn, customer satisfaction.
Purpose
This study examines the extent to which fintech adoption influences the five SERVQUAL dimensions of service quality reliability, responsiveness, assurance, empathy and tangibles and overall customer satisfaction among retail banking customers in Mumbai.
Methodology
A descriptive and analytical research design was adopted. Primary data were collected from a sample of 100 retail banking customers in Mumbai through a structured questionnaire administered on a five-point Likert scale, supplemented by secondary data drawn from journals, RBI publications, NPCI reports and bank disclosures. Percentage analysis, mean analysis and Karl Pearson’s correlation analysis were used to examine the relationships in the data.
Findings
Fintech adoption was found to be positively associated with customer-perceived service quality and satisfaction, with responsiveness and convenience-related dimensions showing the strongest association among Mumbai’s retail banking customers. Respondents in the 21–40 age group and those with higher digital literacy reported significantly greater satisfaction with fintech-enabled banking channels than with traditional branch banking. All three hypotheses were statistically confirmed: a one-sample t-test showed fintech adoption is significantly related to perceived service quality (t = 12.46, p = 0.000); Karl Pearson’s correlation confirmed a strong positive relationship between fintech-driven service quality and customer satisfaction (r = 0.72, p = 0.000); and a one-way ANOVA confirmed that satisfaction differs significantly across age groups (F = 3.85, p = 0.012).
Implications
The findings carry implications for retail banks seeking to refine digital-channel strategy, for regulators concerned with consumer protection and digital financial inclusion, and for future researchers seeking to extend the service-quality literature into the fintech domain.
Keywords: Fintech, Retail Banking, Service Quality, Customer Satisfaction, Digital Transformation, Mumbai, SERVQUAL.
Cite as
Mandeep Singh Ahuja, & Dr. Devendra Lodha. (2026). Digital Transformation in Retail Banking: Fintech’s Effect on Service Quality and Customer Satisfaction in Mumbai. International Journal of Arts, Social Sciences and Humanities, 04(04), 147–156. https://doi.org/10.5281/zenodo.23117140
Dr. (Mrs). Vaishali Nadkarni
Page 157 - 166
Abstract:
The rapid proliferation of artificial intelligence (AI) and big data analytics has fundamentally transformed how organizations understand, predict, and influence consumer behavior. Despite the growing adoption of AI-driven predictive analytics in marketing, there remains limited theoretical integration explaining how these technologies shape consumer decision-making processes. This paper presents a conceptual framework that examines the relationships among AI-based personalization, predictive analytics capability, consumer data quality, and AI recommendation accuracy as independent variables, consumer trust as a mediator, and purchase intention and customer loyalty as dependent variables. Drawing upon the Technology Acceptance Model, the Theory of Planned Behavior, the Stimulus–Organism–Response framework, and Expectation Confirmation Theory, the study synthesizes current literature across AI in marketing, machine learning applications, recommendation systems, customer segmentation, and consumer journey analytics. The proposed framework contributes to the literature by providing a holistic theoretical model that bridges the gap between technological capabilities and behavioral outcomes. Practical implications suggest that organizations should prioritize data quality and algorithmic transparency to foster consumer trust, while ethical considerations regarding privacy and bias in AI models warrant careful governance. The findings offer actionable insights for marketers, technology developers, and policymakers seeking to leverage predictive analytics responsibly in consumer-facing applications.
Keywords: artificial intelligence, predictive analytics, consumer behaviour, personalization, purchase intention, customer loyalty, consumer trust
Cite as
Dr. (Mrs). Vaishali Nadkarni. (2026). Understanding Consumer Behavior Through AI-Driven Predictive Analytics. International Journal of Arts, Social Sciences and Humanities, 04(04), 157–166. https://doi.org/10.5281/zenodo.23118570
Dr. Sachin Joshi
Page 167 - 174
Abstract:
The rapid integration of artificial intelligence into organisational workflows has introduced unprecedented cybersecurity challenges, necessitating a deeper understanding of how users perceive and respond to AI-related risks. This study investigates the mediating role of AI Risk Perception in the relationship between Cyber Security Awareness and Secure AI Usage Behaviour. Drawing on Protection Motivation Theory and employing a cross-sectional survey design, data were collected from 300 respondents across diverse industries using a structured questionnaire measuring three latent constructs. Mediation analysis, conducted using the PROCESS macro with bootstrap confidence intervals, revealed that Cyber Security Awareness has a strong positive effect on AI Risk Perception (path a: β = 0.712, p < 0.001), which in turn positively influences Secure AI Usage Behaviour (path b: β = 0.444, p < 0.001). The direct effect of Cyber Security Awareness on Secure AI Usage Behaviour remained significant (path c′: β = 0.413, p < 0.001), while the indirect effect through AI Risk Perception was also significant (β = 0.316, 95% CI [0.229, 0.404]). These findings support partial mediation, indicating that AI Risk Perception accounts for approximately 43.3% of the total effect. The results underscore the importance of integrating cybersecurity education with AI risk awareness programmes to foster responsible AI adoption.
Keywords: Cyber Security Awareness, AI Risk Perception, Secure AI Usage Behaviour, Mediation Analysis, Responsible AI Adoption, Protection Motivation Theory
Cite as
Dr. Sachin Joshi. (2026). Cyber Security Awareness and Responsible AI Adoption: A Mediation Analysis of AI Risk Perception. International Journal of Arts, Social Sciences and Humanities, 04(04), 167–174. https://doi.org/10.5281/zenodo.23153773
Dr. Balram Gowda
Page 175 - 183
Abstract
The increasing sophistication and volume of financial fraud in digital banking systems have compelled organizations to adopt artificial intelligence (AI) as a core detection mechanism. This study investigated the influence of three critical AI system attributes—perceived AI accuracy, AI speed, and AI reliability—on the perceived effectiveness of AI-based fraud detection among 300 professionals in the banking and financial services sector. Grounded in the DeLone and McLean Information Systems Success Model, a quantitative cross-sectional survey design was employed, and multiple linear regression analysis was conducted. The regression model explained 85.5% of the variance in fraud detection effectiveness (R² = .855, F(3, 296) = 582, p < .001). All three predictors were statistically significant: AI Speed exhibited the strongest standardized effect (β = .350, p < .001), followed by AI Reliability (β = .319, p < .001) and AI Accuracy (β = .298, p < .001). These findings underscore the importance of optimizing not only the accuracy but also the processing speed and reliability of AI fraud detection systems.
Keywords: Artificial Intelligence, Fraud Detection, AI Accuracy, AI Speed, AI Reliability, DeLone and McLean Model, Financial Services
Cite as
Dr. Balram Gowda. (2026). Factors Influencing the Effectiveness of AI-Based Fraud Detection. International Journal of Arts, Social Sciences and Humanities, 04(04), 175–183. https://doi.org/10.5281/zenodo.23154513
Dr. Manojkumar Gupta
Page 184 - 191
Abstract:
The rapid rise of financial influencers popularly known as finfluencers has been one of the most visible developments in India’s financial ecosystem in the last decade. Social media platforms such as YouTube, Instagram, Telegram, Reddit, Facebook and X (formally known Twitter) have provided ordinary individuals with an opportunity to present investment-related content to mass audiences. As India has witnessed full exponential growth and flowering in retail participation in the stock market and mutual funds. Finfluencers have emerged as a crucial bridge between formal financial institutions and young digitally-savvy investors. By providing simplified complex financial decisions they contribute to financial inclusion and digital literacy where formal financial education remains limited. However, their growing influence has also raised significant regulatory, ethical, and economic concerns. The absence of mandatory registration, instances of biased or sponsored recommendations, and the amplification of behavioral biases have created new challenges for investor protection.
The Securities and Exchange Board of India (SEBI) has in response proposed and implemented multiple guidelines since 2023 to address this phenomenon. These include restrictions on intermediaries’ partnerships with finfluencers, mandatory disclosures for paid promotions, and warnings against unregistered advisory services. This research is based exclusively on secondary data that critically examines the rise of finfluencers in India and their implications for individual investment decision-making & SEBI’s regulatory stance. From academic literature, regulatory reports, media coverage, and expert commentaries the paper provides a holistic understanding of the dual role finfluencers play in democratizing finance and exposing retail investors to new risks. It also situates SEBI’s interventions in a comparative global context by drawing parallels with measures adopted by the U.S. Securities and Exchange Commission (SEC) and the UK’s Financial Conduct Authority (FCA). The findings highlight a regulatory dilemma: how to strike a balance between protecting investors from misleading advice while not stifling the positive role of finfluencers in enhancing financial awareness. The study concludes with policy recommendations, enhanced investor education, platform-level accountability, and adaptive regulatory mechanisms to ensure sustainable financial innovation.
Keywords: Finfluencers, SEBI, Retail Investors, Behavioral Finance, Social Media, Regulation
Cite as
Dr. Manojkumar Gupta. (2026). The Rise of Finfluencers and Their Impact on Individual Investment Decisions in India: A Regulatory Perspective with Reference to SEBI Guidelines. International Journal of Arts, Social Sciences and Humanities, 04(04), 184–191. https://doi.org/10.5281/zenodo.23155812
Dr. Priya Ashok Sapkale
Page 192 - 197
Abstract
Green finance has emerged as a critical mechanism for aligning capital allocation with environmental sustainability goals in India. This study examines how instruments such as green bonds, green loans, sustainability-linked finance, and related regulatory frameworks enable Indian businesses to adopt sustainable practices. This study also indicates that small-scale business organizations face limitations in accessing and benefiting from green finance due to inadequate awareness of the available regulatory frameworks, financial instruments, and related opportunities. The awareness of green finance has not yet reached the desired level within the business environment, which limits its effective adoption among small businesses.
Keywords: Green finance, sustainable business practices, conceptual framework.
Cite as
Dr. Priya Ashok Sapkale. (2026). The Role of Green Finance in Promoting Sustainable Business Practices in India: A Conceptual framework. International Journal of Arts, Social Sciences and Humanities, 04(04), 192–197. https://doi.org/10.5281/zenodo.23156627
Mrs. Amita M. Kulkarni
Page 198 - 204
Abstract: Artificial intelligence (AI) has emerged as one of the most transformative forces in contemporary education, reshaping how knowledge is delivered, assessed, and personalized. This literature review examines the evolution, applications, benefits, and challenges of AI in teaching and learning contexts. Drawing on peer-reviewed research and institutional reports published between 2016 and 2024, the review identifies four principal areas of AI application: intelligent tutoring systems, automated assessment and feedback mechanisms, learning analytics, and generative AI for content creation. The findings reveal that AI holds significant promise for enhancing personalized learning, improving student engagement, and reducing educator workloads. However, critical challenges persist, including concerns about data privacy, algorithmic bias, the digital divide, and the potential erosion of the teacher-student relationship. The review concludes that while AI is unlikely to replace human educators, its thoughtful integration can substantially enrich educational outcomes when guided by ethical frameworks and pedagogical best practices.
Keywords: artificial intelligence, education, intelligent tutoring systems, learning analytics, generative AI, personalized learning
Cite as
Mrs. Amita M. Kulkarni. (2026). Artificial Intelligence in Teaching and Learning: Transforming the Future of Education. International Journal of Arts, Social Sciences and Humanities, 04(04), 198–204. https://doi.org/10.5281/zenodo.23187946
Ms. Indira Ulagaraman, and Dr. H. Moideen Batcha
Page 205 - 214
Abstract
The increasing integration of Artificial Intelligence (AI) into human resource management has transformed traditional performance appraisal through data-driven evaluation, continuous feedback, predictive analytics, and automated decision-making. This study evaluates the impact of AI-enabled appraisal practices on millennial career advancement, with particular emphasis on gender equality in Mumbai workplaces. It examines AI-supported performance evaluation, feedback, competency assessment, and data-driven appraisal decisions in relation to career growth, promotion opportunities, professional development, and advancement. It also considers whether perceived gender equality and fairness are associated with career advancement and mediate the relationship between AI-enabled appraisal practices and career advancement. The study adopts a quantitative approach using a structured questionnaire among millennial employees in selected Mumbai workplaces. The findings indicate positive relationships among AI-enabled appraisal practices, perceived gender equality/fairness, and career advancement. The study contributes to the emerging literature on AI in HRM by connecting technology-enabled appraisal, career development, and gender equality. It also highlights the importance of transparency, consistency, human oversight, and ethical governance when implementing AI-supported appraisal systems.
Keywords: Artificial Intelligence, Performance Appraisal, Millennial Employees, Career Advancement, Gender Equality, Human Resource Management, Mumbai Workplaces.
Cite as
Ms. Indira Ulagaraman, & Dr. H. Moideen Batcha. (2026). Evaluating the Impact of AI-Enabled Appraisal Practices on Millennial Career Advancement: A Gender Equality Perspective in Mumbai Workplaces. International Journal of Arts, Social Sciences and Humanities, 04(04), 205–214. https://doi.org/10.5281/zenodo.23188775
Mansi Sudhir Kadam
Page 215 - 222
Abstract
Artificial Intelligence (AI) is increasingly becoming an enabling technology for the collection, validation, analysis and communication of environmental, social and governance (ESG) information and financial information. At the same time, expanding sustainability disclosure requirements are increasing the volume, variety and frequency of non-financial information that organisations must manage. This paper examines how AI can strengthen the relationship between ESG reporting and financial reporting by improving data processing, anomaly detection, disclosure preparation, forecasting, risk identification and digital reporting. The study uses a qualitative, descriptive and exploratory research design based on secondary sources, including international reporting standards, Indian regulatory publications and recent academic literature. Particular attention is given to IFRS S1, IFRS S2, the IFRS Sustainability Disclosure Taxonomy, India’s Business Responsibility and Sustainability Reporting (BRSR) framework and BRSR Core. The findings indicate that AI can improve reporting timeliness, consistency, scalability and decision usefulness, but its effectiveness depends on data quality, governance, explainability, human oversight, cybersecurity and clear accountability. AI should therefore be treated as a controlled reporting technology rather than an autonomous substitute for professional judgement. The paper recommends an integrated AI-enabled reporting architecture with strong data lineage, model governance, human review, assurance controls and ethical safeguards.
Keywords: Artificial Intelligence; ESG; Sustainability Reporting; Financial Reporting; BRSR; IFRS S1; IFRS S2; Data Analytics; Assurance; AI Governance
Cite as
Mansi Sudhir Kadam. (2026). Artificial Intelligence in ESG and Financial Reporting. International Journal of Arts, Social Sciences and Humanities, 04(04), 115–222. https://doi.org/10.5281/zenodo.23191012
Subhash Motiram Shengale
Page 223 - 233
Abstract
Artificial intelligence (AI) is fundamentally reshaping the financial services landscape in India, from algorithmic credit scoring and robo-advisory platforms to AI-powered chatbots handling customer queries. While the adoption of AI-driven financial products has accelerated, consumer trust remains a critical determinant of sustained uptake. This study investigates the influence of three key factors derived from the Technology Acceptance Model (TAM) on consumer trust in AI-enabled financial services in the Indian context: perceived usefulness (PU), perceived security (PS), and perceived ease of use (PEOU). A quantitative research design was employed, utilizing a structured 15-item Likert-scale questionnaire administered to 300 respondents across diverse demographic groups in India. Multiple regression analysis was used to test the hypothesised relationships. The results indicate that the model explains 73% of the variance in consumer trust (R-squared = 0.728), with all three independent variables demonstrating statistically significant positive relationships with consumer trust. Perceived ease of use emerged as the strongest predictor (standardised beta = 0.375), followed by perceived usefulness (standardised beta = 0.310) and perceived security (standardised beta = 0.236). The findings offer practical insights for fintech providers and policymakers seeking to strengthen consumer trust in AI-driven financial services across India.
Keywords: Artificial Intelligence, Consumer Trust, Technology Acceptance Model, Fintech, Financial Services, India
Cite as
Subhash Motiram Shengale. (2026). Factors Influencing Consumer Trust in AI-Enabled Financial Services in India. International Journal of Arts, Social Sciences and Humanities, 04(04), 223–233. https://doi.org/10.5281/zenodo.23191630
Dr. Devanjali Dutta
Page 234 - 243
Abstract
The rapid expansion of digital payment ecosystems in India, anchored by the Unified Payments Interface (UPI), has been accompanied by a sharp rise in payment fraud, eroding consumer confidence and threatening long-term adoption. Artificial intelligence (AI)-driven fraud prevention mechanisms—real-time anomaly detection, behavioural biometrics, and adaptive authentication—are increasingly deployed by payment service providers to mitigate this risk. Drawing on the Expectation-Confirmation Model of Information Systems Continuance (Bhattacherjee, 2001) and the trust literature (Mayer, Davis, & Schoorman, 1995), this study empirically examines the influence of perceived AI security features on customers’ continuance intention toward digital payment applications. A cross-sectional survey of 350 active digital payment users in India was conducted using a structured, five-point Likert-scale instrument. Pearson correlation analysis revealed a very strong, positive, and statistically significant association between AI security features and continuance intention (r = .900, p < .001, 95% CI [.878, .918]). Simple linear regression confirmed that AI security features significantly predict continuance intention, explaining 76.7% of the variance (R² = .767, F(1, 348) = 1147.00, p < .001; β = .876, t = 33.87, p < .001). The findings substantiate the proposition that visible, intelligent fraud-prevention capabilities function as a salient trust-building cue, thereby reinforcing users’ decision to continue using digital payment platforms. The paper discusses theoretical and managerial implications, acknowledges limitations, and outlines directions for future research.
Keywords: artificial intelligence; fraud prevention; digital payments; customer trust; continuance intention; expectation-confirmation model; UPI; India
Cite as
Dr. Devanjali Dutta. (2026). Impact of AI-Based Fraud Prevention on Customer Trust in Digital Payment Systems: An Empirical Investigation of Continuance Intention among Indian Users. International Journal of Arts, Social Sciences and Humanities, 04(04), 234–243. https://doi.org/10.5281/zenodo.23192478
Dr. Amrita A. Jadhav
Page 244 - 250
Abstract:
Artificial intelligence (AI) is rapidly reshaping educational practice worldwide, raising urgent questions about whether its integration supports or undermines the long-term sustainability of education systems. This paper examines the transformation of teaching and learning through AI from a sustainable education perspective, framed by the United Nations Sustainable Development Goal 4 (Quality Education). Drawing on a narrative synthesis of peer-reviewed literature and policy documents published between 2014 and 2024, the study identifies five thematic areas where AI intersects with sustainability: personalized learning, teacher augmentation, equity and inclusion, ethics and privacy, and the environmental footprint of educational AI infrastructure. Findings indicate that AI-enabled personalized learning can improve outcomes, but risks widening equity gaps if access and digital literacy are unevenly distributed. AI offers significant potential to augment rather than replace teachers, while demanding robust ethical governance to address bias, privacy and transparency. The environmental cost of large-scale AI deployment remains an under-recognised sustainability dimension. The paper concludes that sustainable AI in education requires coordinated policy, educator agency, AI literacy for all stakeholders, and a commitment to inclusive, transparent and ecologically responsible design.
Keywords: artificial intelligence in education; sustainable education; SDG 4; personalized learning; AI ethics; teacher augmentation; educational technology
Cite as
Dr. Amrita A. Jadhav. (2026). Transforming Teaching and Learning through Artificial Intelligence: A Sustainable Education Perspective. International Journal of Arts, Social Sciences and Humanities, 04(04), 244–250. https://doi.org/10.5281/zenodo.23205943
Ms. Shivani Pandya, and Dr. Kedar Bhide
Page 251 - 259
Abstract
The growing use of social media for financial information has changed the way retail investors encounter investment-related opinions, market information and product-related content. This study examines whether age, educational qualification and the frequency of consuming financial or investment content through social media are associated with mutual fund investment decision scores among retail investors represented in the Mumbai and Thane region. Primary data was collected from 67 retail investors through a structured questionnaire. A 10-item Likert-scale measure was used to capture social-media-influenced mutual fund investment decision-making and demonstrated good internal consistency (Cronbach’s alpha = 0.839). Descriptive statistics and Spearman’s rank-order correlation were used for the analysis. The results indicate no statistically significant association between age and investment decision scores (ρ = -0.136, p = 0.283), educational qualification and investment decision scores (ρ = -0.034, p = 0.789), or frequency of financial-content consumption through social media and investment decision scores (ρ = 0.162, p = 0.218) at the 5% level of significance. Thus, the study does not find sufficient evidence to reject the respective null hypotheses. The findings suggest that the frequency of exposure to financial content on social media, considered in isolation, may not adequately explain variation in mutual fund investment decision scores. The study highlights the importance of examining the quality and credibility of digital information, investor characteristics and behavioural factors in future research.
Keywords: Retail Investors; Mutual Funds; Social Media; Investment Decisions; Finfluencers; Behavioural Finance; Mumbai and Thane.
Cite as
Ms. Shivani Pandya, & Dr. Kedar Bhide. (2026). Social Media and Mutual Fund Investment Decision-Making among Retail Investors: Evidence from Mumbai and Thane. International Journal of Arts, Social Sciences and Humanities, 04(04), 251–259. https://doi.org/10.5281/zenodo.23210051
Ms. Ajab Tinwala, and Mr. Ravi Singh
Page 260 - 271
Abstract
Artificial intelligence has been one of the most prominent forces which have entered the marketing world recently. The force of being able to make recommendations to individuals, deploy chatbots while assisting them, create AI generated ads and analyse past data to predict future patterns has fundamentally changed the way in which modern consumers are engaged and influenced. This paper attempts to establish if there is a significant relationship between consumer perception towards the use of artificial intelligence in marketing and their acceptance of such marketing approaches amongst college students.
A questionnaire was administered to one hundred and fifty college students using a five point likert scale. Analysis was done using the SPSS statistical tool. Two of the study’s main constructs Perception of AI powered marketing and acceptance of AI powered marketing together with the student’s age, gender, year of study and level of online interaction with brands were determined and subjected to statistical analysis. Descriptive statistics, cronbach’s alpha test, pearson’s correlation, linear regression, independent sample t-test and ANOVA were performed on the dataset.
The study established that both constructs were positively highly reliable (Perception α = 0.913; Acceptance α = 0.969). Additionally, the study established that the two constructs were moderately positively correlated with each other (r = 0.619, p < 0.001), thus rejecting the null hypothesis. However, the study noted that the acceptance of AI powered marketing was highly dependent on the frequency of online shopping for students (ANOVA p < 0.001), though gender and age had no significant effect on either perception or acceptance of AI powered marketing.
The study ultimately concludes that perception greatly influences the general acceptance of AI powered marketing by college students. The paper provides some suggestions on how marketers could make better impressions on customers’ perceptions of AI powered marketing.
Keywords: Artificial Intelligence; AI Powered Marketing; Perception & Acceptance.
Cite as
Ms. Ajab Tinwala, & Mr. Ravi Singh. (2026). Consumer Perception Towards AI-Powered Marketing: A Study Among College Students. International Journal of Arts, Social Sciences and Humanities, 04(04), 260–271. https://doi.org/10.5281/zenodo.23212121
Mr. Ganesha Gundu, and Mr. Ravi Singh
Page 272 - 281
Abstract:
Artificial Intelligence (AI) and Green Finance have emerged as two of the most important forces that have the potential to drive the world towards a sustainable economy. The current paper attempts to explore the role of AI and Green Finance in promoting and supporting a sustainable economy with reference to building a Viksit Bharat 2047. A survey was conducted among 160 respondents, with the results analysed using descriptive statistics, reliability analysis, correlation, regression, and an independent-samples t-test. The results demonstrated that the population studied had a generally positive perception about how AI can support Green Finance and lead the economy towards sustainability. The analysis identified strong awareness about AI as the biggest predictor of increased perception of the integration of AI and Green Finance (r = 0.78, p < .001). Additionally, participants who were aware of Green Finance had significantly higher scores for the perceived level of integration than those who were not (t = 2.65, p = .009). As such, there is reason to reject the null hypothesis, which claimed that there is no connection between the two concepts. Finally, the paper provides recommendations that financial institutions, regulators, and educators can consider in order to promote and support responsible and AI-enabled Green Finance.
Keywords: Artificial Intelligence, Green Finance, Sustainable Economy, ESG, Sustainable Development Goals, Viksit Bharat 2047
Cite as
Mr. Ganesha Gundu, & Mr. Ravi Singh. (2026). A Study of Artificial Intelligence and Green Finance: Building A Sustainable Economy. International Journal of Arts, Social Sciences and Humanities, 04(04), 272–281. https://doi.org/10.5281/zenodo.23242281
Dr. (CA) Nishesh S Vilekar, Ms. Manmeet Kaur
Page 282 - 288
Not Available
Cite as
Dr. (CA) Nishesh S Vilekar, & Ms. Manmeet Kaur. (2026). An Empirical Study on Awareness and Adoption of AI-Based Accounting Software Among Accountants. International Journal of Arts, Social Sciences and Humanities, 04(04), 282–288. https://doi.org/10.5281/zenodo.23243070
Gurkirat Singh, Devansh Modi, and Shubham Raut
Page 289 - 298
Abstract
Product packaging has evolved from a purely functional necessity into a strategic marketing tool that actively shapes how consumers perceive, trust, and ultimately purchase a product. This study examines the influence of packaging on consumer purchasing decisions through a structured survey of 70 valid respondents, supported by a review of existing academic literature and a real-world business case study. The research finds that packaging measurably affects attention, trust, and purchase intent: 44.9% of respondents said attractive packaging genuinely catches their attention, 46.4% said good packaging builds trust, and 46.4% said poor packaging would strongly discourage a purchase. When shown two identical products differing only in packaging quality, with no price attached, 65.7% of respondents chose the better-packaged option, indicating that packaging alone carries substantial purchasing weight. Design and layout emerged as the single most valued packaging element (31.4%), ahead of material, eco-friendliness, product information, shape, and colour. A supporting case study of Tropicana’s 2009 packaging redesign illustrates the commercial risk of poor packaging decisions, with the brand experiencing an estimated 20% sales decline within two months of an unrecognisable redesign. The study also explores how artificial intelligence is beginning to reshape packaging design, testing, and quality control. The study concludes that packaging is not a cosmetic afterthought but a measurable, quantifiable driver of consumer behaviour, and offers practical recommendations for businesses seeking to use packaging as a competitive tool.
Keywords: Product Packaging, Consumer Behaviour, Purchasing Decisions, Packaging Design, Brand Perception, Artificial Intelligence, Marketing.
Cite as
Gurkirat Singh, Devansh Modi, & Shubham Raut. (2026). A Study on Product Packaging and Its Influence on Consumer Purchasing Decisions. International Journal of Arts, Social Sciences and Humanities, 04(04), 289–298. https://doi.org/10.5281/zenodo.23254562
Dr Roshini Udhwani, Dr Nimit Sachdev
Page 299 - 309
Not Available
Cite as
Dr Roshini Udhwani, & Dr Nimit Sachdev. (2026). A study on youth’s perception regarding AI assisted recruitment app and its development. International Journal of Arts, Social Sciences and Humanities, 04(04), 299–309. https://doi.org/10.5281/zenodo.23257658
Nitin Kulkarni
Page 310 - 318
Not Available
Cite as
Nitin Kulkarni. (2026). Artificial Intelligence and Mutual Fund Investment Decisions: A Mediation Analysis of the Role of Investment Confidence. International Journal of Arts, Social Sciences and Humanities, 04(04), 310–318. https://doi.org/10.5281/zenodo.23258429
MS. Kalai Selvi Rajesh, Ms. Tuba Mehboob Belim
Page 319 - 325
Abstract;
Social media has become an important marketing tool for businesses to reach and influence consumers. This study examines the impact of social media marketing on consumer purchase decisions. It focuses on the role of social media advertisements, online reviews, influencer recommendations, and engaging content in shaping consumers’ purchase intentions and buying behaviour. The study aims to understand how consumers use social media information when evaluating products and making purchase decisions. It also explores the extent to which social media platforms create awareness, build trust, and influence consumers’ perceptions of brands and products.
The study considers how factors such as the credibility of online reviews, frequency of advertisements, influencer engagement, and interactive content contribute to consumers’ purchase intentions. In addition, the study seeks to identify the relationship between social media engagement and actual buying behaviour among consumers. The findings are expected to provide insights into the effectiveness of social media marketing and its influence on consumer behaviour. The study can also help businesses develop effective social media strategies to attract, engage, and retain customers. Overall, the research highlights the growing importance of social media as a strategic marketing channel and its role in influencing modern consumer decision-making.
Keywords: Social media, Influence consumers, Purchase decision, Influencer, Online reviews, Attract and retain customers.
Cite as
MS. Kalai Selvi Rajesh, & Ms. Tuba Mehboob Belim. (2026). A Study on the Impact of social media marketing on consumer purchase decision. International Journal of Arts, Social Sciences and Humanities, 04(04), 319–325. https://doi.org/10.5281/zenodo.23263336
Dr. Basukinath G Jha
Page 326 - 343
Abstract
The rapid integration of artificial intelligence (AI) into the financial sector has fundamentally transformed the way financial institutions operate, manage risk, and deliver services to customers. Despite the growing adoption of AI technologies across banking, insurance, and investment management, there remains a limited understanding of how the perceived benefits and perceived challenges of AI collectively shape the adoption behavior of finance professionals and organizations. This study addresses this gap by developing and empirically testing a conceptual model that examines the influence of perceived benefits (PB) and perceived challenges (PC) on AI adoption (AD) in the financial services industry.
Drawing on the Technology Acceptance Model (TAM), the Diffusion of Innovations (DOI) theory, and the Unified Theory of Acceptance and Use of Technology (UTAUT), this research employs a quantitative survey methodology. A structured questionnaire was administered to 100 respondents working in or associated with the financial sector. The instrument comprised 24 items distributed across three constructs: Perceived Benefits of AI (8 items, α = 0.922), Perceived Challenges of AI (8 items, α = 0.931), and AI Adoption in Finance (8 items, α = 0.911). Data were analyzed using descriptive statistics, Pearson correlation analysis, and multiple linear regression.
The results of the multiple linear regression analysis revealed that the model explained 38.9% of the variance in AI adoption (R² = 0.389, F(2,97) = 30.87, p < .001). Perceived benefits emerged as a strong and statistically significant positive predictor of AI adoption (β = 0.580, p < .001), while perceived challenges had a statistically significant but weaker negative effect (β = -0.162, p < .05). The regression equation was: AI Adoption = 0.884 + 0.617(PB) – 0.162(PC). These findings underscore that while perceived benefits are the primary driver of AI adoption, perceived challenges exert a modest inhibiting effect, suggesting that organizations can accelerate AI adoption by strategically emphasizing tangible benefits while proactively addressing recognized challenges.
Keywords: artificial intelligence, AI adoption, financial services, perceived benefits, perceived challenges, Technology Acceptance Model, multiple linear regression
Cite as
Dr. Basukinath G Jha. (2026). Artificial Intelligence and the Future of Finance: An Empirical Study of Adoption, Benefits and Challenges. International Journal of Arts, Social Sciences and Humanities, 04(04), 326–343. https://doi.org/10.5281/zenodo.23275741
Yuvraj Chauhan, Sahil Surve, Shreya Parab
Page 344 - 351
Abstract
Over-the-top (OTT) streaming platforms have moved beyond using artificial intelligence (AI) purely for content recommendation and are increasingly deploying it to set and personalise the price a subscriber sees. This study examines consumer awareness, comfort, and fairness perceptions surrounding AI-driven pricing on OTT platforms through a structured survey of 70 valid respondents, supported by a review of existing academic literature and a real-world industry case study. The research finds that awareness of algorithmic pricing is only moderate while fairness concern is strong: 52.9% of respondents said they were aware that OTT platforms use algorithms to personalise price, only 38.6% said they were comfortable paying a different price than another user for the same plan, and 61.4% said it would feel unfair if a friend paid less for an identical plan a gap of nearly 23 points between discomfort and perceived unfairness. When directly offered a choice between a plainly stated fixed price and a cheaper AI-personalised price, 51.4% of respondents chose the fixed price over the AI-personalised alternative, indicating that transparency outweighs the promise of a discount. Price transparency emerged as the single most valued factor in a subscription decision (34.3%), ahead of content library size, ad-free experience, personalised recommendations, and multi-profile sharing. A supporting case study of Netflix’s password-sharing crackdown and ad-supported tier rollout illustrates that a well-executed, AI-enabled monetisation strategy can still deliver substantial subscriber and revenue growth even amid visible consumer frustration. The study concludes that AI-driven pricing is not a purely technical back-end function but a measurable driver of consumer trust, and that long-term commercial success depends on pairing algorithmic sophistication with genuine transparency toward the consumer.
Keywords: AI-Driven Pricing, OTT Platforms, Dynamic Pricing, Consumer Behaviour, Price Fairness, Personalisation, Subscription Economy.
Cite as
Yuvraj Chauhan, Sahil Surve, & Shreya Parab. (2026). A Study on AI-Driven Pricing Strategies Used by OTT Platforms. International Journal of Arts, Social Sciences and Humanities, 04(04), 344–351. https://doi.org/10.5281/zenodo.23276746
Dr. Bhavana Trivedi, Ms. Prapti Gandhi
Page 352 - 360
Abstract:
The rapid expansion of the Pradhan Mantri Bhartiya Janaushadhi Pariyojana (PMBJP) has significantly enhanced access to affordable generic medicines across India through a growing network of Jan Aushadhi Kendras (JAKs). However, the long-term success of this network relies heavily on consistent medicine availability. Operational challenges—such as localized stockouts, inventory imbalances, and expiry-related wastage—disrupt supply chains and threaten patient access to affordable healthcare. This highlights a critical need to examine how emerging tools like Artificial Intelligence (AI) and predictive data analytics can optimize inventory management within the PMBJP framework.
The purpose of this study is to examine the potential of AI-based demand forecasting and data-driven inventory models to maximize medicine availability and improve asset turnover at JAKs. The study adopts a descriptive and analytical research design based on secondary data sourced from PMBI and PIB publications, government reports, and peer-reviewed literature on AI applications in pharmaceutical logistics. Trend analysis, comparative evaluations, and qualitative synthesis are utilized to assess PMBJP’s unique operating environment against empirical evidence from comparable health systems.
Findings from parallel healthcare settings show that AI-enabled inventory systems can reduce stockouts by 30–55%, increase inventory turnover by up to 70%, and cut expired inventory losses by 20–33%. While these figures do not represent direct empirical data from individual JAK outlets, they provide robust conceptual support for AI integration. Crucially, the study identifies PMBI’s existing IT-enabled supply chain infrastructure and digital ecosystem as an ideal, mature foundation to host these future AI applications.
Future research should focus on developing and testing JAK-specific AI models using store-level sales, inventory, and localized demand data. Implementing such automated decision-support models could systematically eliminate stockouts, curb chemical wastage, and strengthen overall supply chain resilience. Ultimately, this technological transition supports more reliable public access to affordable medicines, contributing directly to a highly inclusive and sustainable public healthcare system.
Keywords: Jan Aushadhi Kendras, AI, inventory management, medicine availability, stockouts, PMBJP
Cite as
Dr. Bhavana Trivedi, & Ms. Prapti Gandhi. (2026). AI & Data-Driven Inventory Management for Improving Medicine Availability at Jan Aushadhi Kendras. International Journal of Arts, Social Sciences and Humanities, 04(04), 352–360. https://doi.org/10.5281/zenodo.23280644
Simrandeep Kaur Marwaha
Page 361 - 370
Abstract:
Artificial Intelligence (AI) is increasingly transforming the way businesses understand consumers, analyse behavioural patterns, and make marketing and business decisions. The present study, “An Empirical Study on AI-Driven Consumer Insights,” examines the role of AI in generating consumer insights, analysing consumer behaviour, influencing purchasing decisions, improving personalised marketing, and supporting business decision-making. The study also considers the importance of ethical AI practices, privacy, transparency, trust, and responsible use of consumer data. Primary data were collected from 128 respondents from Mumbai, and the data were analysed using tabulation and the Chi-Square test. The findings indicate statistically significant responses across all six hypotheses. The results suggest that respondents perceive AI as an important tool for generating consumer insights, improving consumer behaviour analysis, influencing purchasing decisions, enhancing personalised marketing, and supporting business decisions. The findings also highlight the importance of ethical AI practices in consumer data collection. The study concludes that AI-driven consumer insights can provide significant value to businesses when implemented effectively, transparently, and responsibly.
Keywords: Artificial Intelligence, Consumer Insights, Consumer Behaviour, Personalised Marketing, Purchasing Behaviour, Business Decision-Making, Ethical AI.
Cite as
Simrandeep Kaur Marwaha. (2026). An Empirical Study on AI-Driven Consumer Insights. International Journal of Arts, Social Sciences and Humanities, 04(04), 361–370. https://doi.org/10.5281/zenodo.23281628
Dr. Amandeep Singh Manku
Page 371 - 390
Abstract:
Artificial Intelligence (AI) tools such as ChatGPT, Gemini, Microsoft Copilot, and Claude have rapidly permeated higher education, fundamentally altering the ways in which students learn, conduct research, and prepare for careers. Despite their widespread availability, the extent to which students adopt these tools varies considerably, influenced by factors such as perceived usefulness, ease of use, AI literacy, trust, and ethical awareness. This study investigates the determinants of AI tool adoption among 300 undergraduate and postgraduate students and examines the association between AI tool adoption and perceived employability skills. Employing a quantitative, survey-based research design with a structured five-point Likert scale questionnaire, data were analysed using multiple linear regression, simple linear regression, and independent samples t-test. The multiple regression model explained 34.1% of the variance in AI adoption (R² = 0.341, F(5, 294) = 30.4, p < .001), with AI literacy emerging as the strongest predictor (β = 0.304, p < .001), followed by perceived ease of use (β = 0.269), trust in AI (β = 0.269), perceived usefulness (β = 0.263), and ethical awareness (β = 0.164). The simple regression demonstrated that AI adoption significantly predicted employability skills (R² = 0.349, β = 0.591, p < .001). Furthermore, postgraduate students exhibited significantly higher AI adoption levels than undergraduate students, t(298) = 2.74, p = .007, with a small effect size (Cohen’s d = 0.334). These findings underscore the necessity for educational institutions to integrate AI literacy training and responsible AI usage policies into curricula, thereby equipping students with the digital competencies demanded by AI-driven workplaces. The study contributes to the emerging literature on AI adoption in higher education by integrating multiple adoption determinants within a single empirical framework and linking them to employability outcomes.
Keywords: Artificial Intelligence, AI Adoption, AI Literacy, Employability Skills, Higher Education, Technology Acceptance.
Cite as
Dr. Amandeep Singh Manku. (2026). Factors Influencing the Adoption of Artificial Intelligence Tools and Their Association with Employability Skills among Undergraduate and Postgraduate Student. International Journal of Arts, Social Sciences and Humanities, 04(04), 371–390. https://doi.org/10.5281/zenodo.23317041
Prof. Dr. Rita Khatri, Ms. Pooja S. Gupta
Page 391 - 401
Abstract:
The rapid growth of social media influencer marketing has transformed the way Fast-Moving Consumer Goods (FMCG) brands communicate with consumers. At the same time, increasing environmental awareness has encouraged brands to promote sustainability-related claims, raising concerns about greenwashing. This study analytically examines the relationship between social media influencer marketing and greenwashing practices in FMCG brands, with a focus on influencer credibility, consumer awareness, trust, brand credibility, and purchase intention. The study highlights the importance of transparent and authentic sustainability communication and examines how misleading environmental claims communicated through influencers may influence consumer perceptions and behaviour. The study emphasizes the need for evidence-based environmental claims, responsible influencer endorsements, and greater consumer awareness to promote ethical and credible sustainability marketing in the FMCG sector.
Keywords: Social Media Influencer Marketing, Greenwashing, FMCG Brands, Sustainability Communication, Consumer Trust, Brand Credibility, Environmental Claims, Consumer Behaviour, Green Marketing, Purchase Intention.
Cite as
Prof. Dr. Rita Khatri, & Ms. Pooja S. Gupta. (2026). An Analytical Study of Social Media Influencer Marketing and Greenwashing Practices in FMCG Brands. International Journal of Arts, Social Sciences and Humanities, 04(04), 391–401. https://doi.org/10.5281/zenodo.23335736
Ms. Anjali Ved, Dr. Parag Ajgaonkar
Page 402 - 408
Abstract:
Artificial Intelligence (AI) is increasingly enabling restaurants to deliver personalized customer experiences while supporting sustainable business practices. However, the effectiveness of AI-enabled interventions depends on understanding the behavioural factors that influence consumer food waste. This study examines consumer behaviour related to restaurant food waste and explores how behavioural insights can inform AI-driven personalization strategies for food waste reduction. The research is grounded in the Theory of Planned Behaviour (TPB) and is based on primary data collected from 100 restaurant consumers in the Mumbai Suburban region using a structured questionnaire. The study assessed six behavioural constructs: awareness, attitude, behavioural factors, psychological factors, social factors, and willingness to act towards food waste reduction. Descriptive analysis revealed that respondents demonstrated relatively high levels of awareness (Mean = 3.89) and positive attitudes (Mean = 3.92) towards reducing food waste. Psychological factors (Mean = 3.81) and social factors (Mean = 3.64) also indicated favourable behavioural orientations. However, behavioural practices recorded comparatively lower scores (Mean = 3.45), suggesting an attitude–behaviour gap in responsible food consumption. Respondents also expressed a positive willingness to adopt food waste reduction practices (Mean = 3.76), indicating the potential for targeted behavioural interventions. Based on these findings, the study proposes an AI-driven consumer insight framework that can support restaurants in designing personalized food waste reduction strategies, including portion-size recommendations, smart menu suggestions, and digital nudges. Rather than positioning AI as a replacement for managerial decision-making, the framework demonstrates how consumer behavioural data can be leveraged to enhance AI-enabled customer engagement and sustainability initiatives. The study contributes to the growing discourse on AI in Business and Marketing by illustrating the role of behavioural insights in developing customer-centric and sustainable restaurant practices. The findings offer practical implications for restaurant managers seeking to integrate AI-enabled personalization with food waste reduction initiatives in the HORECA sector.
Keywords: Artificial Intelligence, Consumer Behaviour, Food Waste, Restaurant Management, Theory of Planned Behaviour, Sustainability, HORECA.
Cite as
Ms. Anjali Ved, & Dr. Parag Ajgaonkar. (2026). Consumer Behavioural Insights for AI-Enabled Personalized Food Waste Reduction in Restaurants. International Journal of Arts, Social Sciences and Humanities, 04(04), 402–408. https://doi.org/10.5281/zenodo.23338605
Dr. Kishor Chauhan
Page 409 - 422
Abstract:
Artificial intelligence (AI) has fundamentally transformed the capacity of organisations to deliver personalised recommendations, communications, customer service and digital experiences at scale. This study develops and empirically tests a comprehensive model linking AI-based personalisation with customer loyalty through the mechanisms of trust and customer satisfaction, while simultaneously examining AI service quality and privacy concern as contextual factors. Using a quantitative cross-sectional design, data were collected from 400 digital-service users through a structured 5-point Likert-scale questionnaire. The analysis encompasses descriptive statistics, reliability testing, KMO and Bartlett’s tests, correlation analysis, multiple regression, mediation and moderation analysis, and selected demographic tests.
The results demonstrate acceptable internal consistency for all constructs, with Cronbach’s alpha values ranging from .801 to .865. The KMO value was .916 and Bartlett’s test was statistically significant, indicating that the item correlation matrix was suitable for factor-oriented analysis. AI-based personalisation exhibited a positive association with customer loyalty (β = .380, p < .001), explaining 14.4% of its variance in the simple regression. The full regression model explained 42.4% of the variance in loyalty. A bootstrap test of the serial indirect pathway through trust and satisfaction was positive (indirect effect = .060, 95% CI [.040, .085]). Privacy concern significantly moderated the AI personalisation–loyalty relationship (interaction β = .060, p = .040). The findings contribute to the growing body of knowledge on AI-driven personalisation by demonstrating that trust and satisfaction serve as critical mediating mechanisms, while privacy concerns represent an important boundary condition.
Keywords: artificial intelligence; AI personalisation; customer loyalty; trust; customer satisfaction; privacy concern; AI service quality; digital services
Cite as
Dr. Kishor Chauhan. (2026). Impact of AI-Based Personalised Services on Customer Loyalty. International Journal of Arts, Social Sciences and Humanities, 04(04), 409–422. https://doi.org/10.5281/zenodo.23339435
Ms. Kirandeep Kaur, Mrs. Harpreet Kaur
Page 423 - 436
Abstract:
The integration of artificial intelligence into environmental sustainability strategies presents a critical dilemma known as the “Green Tech Paradox.” While AI offers unprecedented capabilities to optimize resource allocation, predict climate patterns, and enhance energy efficiency across industrial sectors, its deployment carries substantial, often overlooked, ecological costs. This research provides a critical analysis of the dual role AI plays as both a catalyst for eco-innovations and a significant contributor to environmental degradation.
On the positive frontier, AI-driven applications accelerate sustainability by enabling smart power grids, improving carbon capture systems, facilitating precision agriculture, and modeling complex environmental crises. Machine learning algorithms enhance operational efficiency, reducing waste and carbon footprints in manufacturing and logistics. However, these beneficial outcomes are counterbalanced by the escalating physical footprint of AI infrastructure. The training and continuous execution of complex neural networks demand massive amounts of electricity, heavily relying on fossil-fuel-dominant energy grids.
Furthermore, data centers consume millions of liters of freshwater daily for cooling mechanisms, accelerating localized water scarcity, while the rapid obsolescence of specialized hardware generates growing volumes of hazardous electronic waste.
This paper evaluates this structural tension through a multidimensional synthesis of secondary data, life-cycle assessments, and resource consumption metrics across global data centers. The analysis reveals that without stringent regulatory frameworks and sustainable design interventions, the environmental degradation caused by AI’s hardware lifecycle risks surpassing its ecological benefits. Ultimately, the study advocates for a paradigm shift toward “Green AI”—prioritizing algorithmic efficiency, renewable energy integration for computing infrastructure, sustainable hardware lifecycle management, and policy-driven eco-design standards. By establishing transparent auditing protocols, researchers and industry leaders can mitigate the hidden ecological liabilities of artificial intelligence, ensuring that modern technological advancement aligns genuinely with global net-zero targets and long-term planetary boundaries.
Keywords: Green AI, Climate change mitigation, Sustainable development goals (SDGs), Environmental governance, Lifecycle assessment of AI.
Cite as
Ms. Kirandeep Kaur, & Mrs. Harpreet Kaur. (2026). GREEN TECH PARADOX An Analysis of AI in Environment Sustainability. International Journal of Arts, Social Sciences and Humanities, 04(04), 423–436. https://doi.org/10.5281/zenodo.23341246
Dr. Pankaj Nandurkar, Mr. Pradeep Gupta, Mr. Sangram Andhe and Dr. Krishnakant Lasune
Page 437 - 447
Abstract:
Generative artificial intelligence (GenAI) is transforming advertising by enabling marketers to create persuasive text, images, and other promotional content rapidly and at lower cost. At the same time, the ability of generative systems to produce highly realistic content creates concerns about deception, misinformation, transparency, and the credibility of consumer-facing information. The present study examines the consumer consequences of AI-generated false and misleading advertising, with particular emphasis on hallucinated product claims. The study focuses on whether AI-generated hallucinated product claims influence perceived deception, AI skepticism, and perceived information reliability, and whether these consumer responses subsequently shape purchase intention. Drawing on the stimulus-organism-response (S-O-R) framework, the study conceptualizes hallucinated product claims as the stimulus, the three consumer evaluations as the organism, and purchase intention as the response. A quantitative, cross-sectional, deductive research design was adopted. Data were collected from 320 business-school students and faculty members through a structured questionnaire using a five-point Likert scale, and the model was assessed using SmartPLS. The reported measurement results show Cronbach’s alpha values above 0.70, composite reliability above the stated threshold, and average variance extracted above 0.50 for all constructs, indicating acceptable reliability and convergent validity. The reported discriminant-validity matrix also supports construct distinctiveness. The structural model reports a VIF range of 1.47-2.24 and an R² of 0.421 for purchase intention. Four of the six hypotheses are supported. AI-generated hallucinated product claims significantly increase perceived deception and significantly reduce perceived information reliability. Perceived deception significantly reduces purchase intention, while perceived information reliability significantly increases purchase intention. The paths from hallucinated product claims to AI skepticism and from AI skepticism to purchase intention are not statistically significant. The findings indicate that message-level judgments of deception and information reliability are more consequential for purchase intention in the reported model than generalized skepticism toward AI. The study extends the S-O-R perspective to the dark side of GenAI in advertising and highlights the importance of factual verification, transparency, human oversight, disclosure, and consumer AI literacy.
Keywords: Generative artificial intelligence; AI hallucinations; misleading advertising; perceived deception; AI skepticism; perceived information reliability; purchase intention; stimulus-organism-response theory.
Cite as
Dr. Pankaj Nandurkar, Mr. Pradeep Gupta, Mr. Sangram Andhe, & Dr. Krishnakant Lasune. (2026). The Dark Side of Generative AI in Marketing: AI-Generated False and Misleading Advertising and Its Consumer Consequences. International Journal of Arts, Social Sciences and Humanities, 04(04), 437–447. https://doi.org/10.5281/zenodo.23389669
Ms Swarangi Sayli Subhash Marchande, Dr. Amrin Moger
Page 448 - 454
Abstract:
Artificial Intelligence (AI) has emerged as a transformative force in the advertising industry, enabling brands to produce video content that is personalized, visually engaging, scalable, and cost‑effective. Despite these advantages, the proliferation of AI in advertising raises significant ethical concerns, particularly regarding authenticity, transparency, privacy, misinformation, and deepfakes. These issues are especially relevant among college students, who encounter advertisements extensively through social media platforms. This study investigates the perceptions of AI‑generated video advertisements among college students in Mumbai, with a focus on ethics, authenticity, and trust. A quantitative research design was employed, and primary data were collected through a structured questionnaire administered via Google Forms. The final sample consisted of 91 respondents, who provided insights into their social media usage, perceptions of AI‑driven advertising, and concerns related to ethical practices. The findings indicate that lack of transparency was the most prominent ethical concern (42%), followed by privacy misuse (30%) and misleading content or deepfakes (20%). Respondents emphasized the importance of clear disclosure of AI involvement, truthful communication, human oversight, protection of personal data, and avoidance of misleading or stereotypical representations. The study concludes that students do not categorically reject AI‑generated advertising; rather, their acceptance is contingent upon responsible and transparent application of AI technologies. Overall, the results highlight that technological innovation in advertising must be supported by authenticity, ethical communication, transparency, and human oversight to strengthen trust among college students in Mumbai. These insights contribute to ongoing debates on digital ethics and the responsible integration of AI in advertising practices.
Keywords: Artificial Intelligence, AI-Generated Video Advertisements, College Students, Advertising Ethics, Social Media
Cite as
Ms Swarangi Sayli Subhash Marchande, & Dr. Amrin Moger. (2026). AI‑Driven Video Advertisements and Ethical Concerns: Exploring College Student Perceptions in Mumbai. International Journal of Arts, Social Sciences and Humanities, 04(04), 448–454. https://doi.org/10.5281/zenodo.23390986
Isha Singadiya, Harpreet Kaur
Page 455 - 462
Abstract:
The rapid adoption of Artificial Intelligence (AI) has increased the collection, processing and automated use of personal data. While AI supports innovation, personalization and efficiency, data-intensive AI systems create concerns relating to privacy, consent, transparency, profiling, security, surveillance and individual control. In India, the Digital Personal Data Protection Act, 2023 provides a statutory framework for digital personal data, while the Digital Personal Data Protection Rules, 2025 provide the implementation framework.
This study examines privacy awareness and stakeholder perceptions of data-protection regulation in an AI-driven environment using secondary empirical evidence obtained from an existing published research/data source. The analysis focuses on privacy awareness, awareness of selected rights under the DPDP Act, perceived regulatory effectiveness, privacy-business balance and grievance redressal. Reported statistical evidence indicates higher privacy-awareness scores for professional stakeholders than laypersons, while organizational communication is associated with stronger awareness of privacy rights.
Keywords: Artificial Intelligence, Data Privacy, Privacy Awareness, AI Regulation, DPDP Act 2023, AI Governance, Data Protection, Ethics.
Cite as
Isha Singadiya, & Harpreet Kaur. (2026). Data Privacy Challenges in the Age of Artificial Intelligence: A Study of Privacy Awareness and AI Regulation. International Journal of Arts, Social Sciences and Humanities, 04(04), 455–462. https://doi.org/10.5281/zenodo.23392550
Navneet Tiwari, Harpreet Kaur
Page 463 - 473
Abstract
Artificial Intelligence (AI) has transformed digital marketing by enabling e-commerce platforms to analyse consumer behaviour and provide personalized product recommendations. Recommendation systems can reduce information overload, improve product relevance and support customer decision-making. Their effectiveness, however, depends on factors such as recommendation frequency, perceived helpfulness and perceived accuracy.
This study examines consumer purchase behaviour using the publicly available Amazon Consumer Behaviour Dataset hosted on Kaggle. The dataset contains 602 observations and 23 variables covering demographics, purchase frequency, browsing behaviour, personalized recommendations, recommendation helpfulness, rating accuracy, reviews, Save-for-Later activity and shopping satisfaction. The study follows a quantitative, explanatory secondary-data design.
Reported statistical analyses of the same dataset show significant positive associations between purchase frequency and browsing frequency, Save-for-Later frequency and review helpfulness. A K-means analysis also identifies two distinct behavioural segments. Recommendation-specific analysis reports a positive association between personalized recommendation frequency and rating accuracy. Because the dataset contains Purchase_Frequency rather than a direct Purchase_Intention measure, the findings are interpreted as evidence of consumer purchase behaviour.
Keywords: Artificial Intelligence, Personalized Recommendations, Recommendation Systems, Consumer Behaviour, Purchase Frequency, E-commerce, Amazon, Digital Marketing.
Cite as
Navneet Tiwari, & Harpreet Kaur. (2026). Impact of AI-Based Personalized Recommendations on Consumer Purchase Behaviour: A Study Using the Amazon Consumer Behaviour Dataset. International Journal of Arts, Social Sciences and Humanities, 04(04), 463–473. https://doi.org/10.5281/zenodo.23393494
Mrs. Jagatheeswari Deivamuthu Somasundari Nadar, Dr. Ramraj T. Nadar
Page 474 - 483
Abstract
The research study seeks to investigate the adoption of Artificial Intelligence enabled digital banking on micro, small and medium enterprises led by women in Mumbai suburbs. Though AI is transforming the banking and financial services by improving easy access to banking services, efficiency and security, still informal MSME’S faces lack of reach of such services due to various infant stages especially among women entrepreneurs.
The proposed study aims to identify the vast potential in the MSME sector undertaken by women particularly in Mumbai suburbs. The area under study have limited access to digital banking and financial services. Though MSMEs form the backbone of developing and developed nations, yet there is a constant barrier in adopting digital banking services.
This paper investigates the transformative role of AI enabling for easy digital banking accessibility in their day-to-day financial transactions especially cross-sectional groups. The study evaluates the readiness of technology adoption, organizational compatibility and perceived trust with special reference TAM (Technology Acceptance Model) integration in women-led MSMEs.
The study explores the level of awareness about AI it is ways to AI adoption, and enabling factors in it and its outcome on MSMEs performance. Adoption correlates positively with women entrepreneurs, digital literacy, access to finance and supportive local networks.
The research study more specific to the Mumbai suburban area. Most of the MSMEs run by women entrepreneurship are informal in doing business. They lack proper documentation related to their business and financial transactions. Sometime this may lead to bias. Due to the current significant development of Artificial Intelligence in the financial sector the research study related to adoption may tend to shift to other newer features.
The study concludes by developing a comprehensive framework showing the drivers influencing the adoption of AI in digital banking among the women-led entrepreneurs. Further in the research it provides insights to the policy makers to consider the women-led entrepreneurs requirements in their financial polices
Keywords: Artificial Intelligence (AI), Digital banking, MSME, TAM (Technology Acceptance Model) integration.
Cite as
Mrs. Jagatheeswari Deivamuthu Somasundari Nadar, & Dr. Ramraj T. Nadar. (2026). Adoption of AI-enabled Digital Banking Empowering Informal Women-Led MSMEs in Mumbai Suburbs. International Journal of Arts, Social Sciences and Humanities, 04(04), 474–483. https://doi.org/10.5281/zenodo.23411112
Ms. Zulfa Khan, Mr. Tarkar Jeet Ravindranath
Page 484 - 493
Abstract
Artificial Intelligence (AI) is becoming an important technology in transforming education and skill development. The National Education Policy (NEP) 2020 emphasizes technology-enabled learning, multidisciplinary education, vocational education, critical thinking, creativity, and the development of future-ready skills. The integration of AI into education can support these objectives by providing personalized learning, intelligent assessment, automated feedback, digital learning resources, and improved teaching and learning processes. AI can help students learn according to their individual needs, improve academic performance, and access educational resources more effectively. It can also contribute to the development of digital literacy, problem-solving, creativity, communication, and other skills required in the changing world of work. For teachers, AI can assist in lesson planning, assessment, feedback, and identifying students’ learning needs, thereby improving the overall efficiency of the education system. However, the successful integration of AI also presents several challenges. These include the digital divide, unequal access to technology, inadequate digital infrastructure, data privacy and security concerns, ethical issues, limited awareness, and insufficient training among teachers and educators. Addressing these challenges is essential to ensure that AI benefits all learners equally and is used responsibly. This study examines the role of AI in education and skill development and explores its alignment with the objectives of NEP 2020. It focuses on the opportunities offered by AI, as well as the challenges involved in its implementation. The study also suggests measures such as improving digital infrastructure, providing teacher training, promoting AI awareness, ensuring data privacy, and establishing ethical guidelines for responsible AI use. Overall, the study highlights that the effective and responsible integration of AI can strengthen the objectives of NEP 2020. By combining technological innovation with inclusive and learner-centred approaches, AI can contribute to a more personalized, accessible, innovative, and future-ready education system in India.
Keywords: AI Personalized learning, intelligent assessment, digital learning resources, language support, and improved teaching methods.
Cite as
Ms. Zulfa Khan, & Mr. Tarkar Jeet Ravindranath. (2026). Study on AI Education and Skill Development: NEP 2020 and AI Integration. International Journal of Arts, Social Sciences and Humanities, 04(04), 484–493. https://doi.org/10.5281/zenodo.23411382
Aatif Ahmed Khan
Page 494 - 505
Abstract
Artificial Intelligence (AI) is transforming professional services through automation, information processing, analytics and decision support. Legal and accounting professions, which rely on expertise, judgment, confidentiality and compliance, are increasingly exposed to AI technologies, though adoption and perceived impacts may differ between the two fields.
This study examines and compares AI adoption and professional transformation among legal and accounting professionals in Mumbai. It focuses on AI awareness, adoption, perceived benefits, professional transformation, ethical concerns, skills, employment implications and future adoption intentions. A quantitative research design using a structured questionnaire will be adopted, with data analysed through descriptive, reliability, correlation and comparative statistical techniques. The study aims to identify similarities, differences and factors influencing AI adoption, providing insights for effective AI training, governance and responsible adoption.
Keywords: Artificial Intelligence, AI Adoption, Legal Professionals, Accounting Professionals, Professional Transformation, Technology Adoption, Mumbai
Cite as
Aatif Ahmed Khan. (2026). Artificial Intelligence Adoption and Professional Transformation: A Comparative Study of Legal and Accounting Professionals in Mumbai. International Journal of Arts, Social Sciences and Humanities, 04(04), 494–505. https://doi.org/10.5281/zenodo.23413983
Rajvinder kaur Gill
Page 506 - 526
Abstract
This study examines how artificial intelligence (AI) is reshaping demand for finance labour in India, using exclusively secondary, publicly verifiable evidence drawn from online job-market trackers and official sources. The analysis synthesises monthly hiring indices (Naukri JobSpeak; foundit Insights Tracker), AI-posting share trackers (Indeed), skills projections (World Economic Forum), task-based exposure measures (International Labour Organization; International Monetary Fund; OECD), and India-specific labour-survey research based on the Periodic Labour Force Survey, together with Government of India, Reserve Bank of India and NASSCOM documents. Reported indicators are compared descriptively and across finance-related occupational groups; no new microdata are collected, and the few calculations performed by the author are explicitly labelled as such. Three findings stand out. First, AI-related hiring in India has consistently outgrown aggregate white-collar hiring, with reported year-on-year growth in AI/ML roles of 25-42 per cent during 2024-2026 against flat or negative comparator segments. Second, exposure is highly uneven across finance occupations: routine clerical and front-office roles (bank tellers, data entry, accounting and bookkeeping) are concentrated at the automation-exposed end, while fintech engineering, AI/data specialist and compliance-oriented roles are expanding. Third, most evidence supports task-level augmentation rather than occupational substitution, implying that transformation, not wholesale job destruction, is the central policy problem. The paper discusses implications for finance professionals, higher education and skills policy, and states its limitations, including the absence of publicly available raw Indian job-posting microdata.
Keywords: artificial intelligence; finance jobs; banking; FinTech; online job postings; skills; India; labour market transformation
Cite as
Rajvinder kaur Gill. (2026). Artificial Intelligence and the Transformation of Finance Jobs in India: Evidence from Online Job-Market Data. International Journal of Arts, Social Sciences and Humanities, 04(04), 506–526. https://doi.org/10.5281/zenodo.23473978
Ms. Janet Sagayanathan
Page 527 - 531
Abstract
Generative Artificial Intelligence (GenAI) has emerged as a transformative technology in higher education, significantly influencing the way students learn, access information, complete academic tasks, and develop professional skills. Tools such as ChatGPT, Google Gemini, Microsoft Copilot, and Canva AI have become increasingly popular among commerce students for assignments, presentations, exam preparation, and concept clarification. While these technologies offer opportunities for personalized and efficient learning, their growing use also raises concerns regarding academic integrity, ethical practices, critical thinking, and dependency on AI-generated content.
This study aims to examine the adoption and responsible use of Generative AI tools among commerce students in higher education and to evaluate their contribution towards sustainable learning. The research adopts a descriptive and analytical research design using primary data collected through a structured questionnaire administered to commerce students. The study focuses on students’ awareness of GenAI tools, frequency and purpose of usage, responsible AI practices, AI dependency, and the perceived impact of AI on academic productivity and sustainable learning outcomes.
The study is expected to provide insights into how commerce students integrate Generative AI into their academic learning and whether they use these tools responsibly by verifying information, avoiding plagiarism, and using AI as a learning aid rather than a substitute for independent thinking. It also explores the relationship between AI adoption, ethical usage, and sustainable learning practices in the context of higher education.
The findings of this research are expected to assist educators, higher education institutions, and policymakers in designing AI literacy initiatives, promoting responsible AI usage, and developing pedagogical practices aligned with the objectives of sustainable and inclusive education. The study contributes to the growing body of literature on Artificial Intelligence in commerce education by emphasizing the importance of balancing technological innovation with ethical responsibility and lifelong learning in higher education.
Keywords: Generative Artificial Intelligence (GenAI), Responsible AI, Sustainable Learning, Commerce Students, Higher Education, AI Ethics, Academic Productivity, Digital Literacy.
Cite as
Ms. Janet Sagayanathan. (2026). A Study on the Adoption and Responsible Use of Generative AI Tools for Sustainable Learning among Undergraduate Students. International Journal of Arts, Social Sciences and Humanities, 04(04), 527–531. https://doi.org/10.5281/zenodo.23474434
Ms. Kaleeswari Soundrapandi
Page 532 - 539
Abstract
Fraud is one of the major challenges faced by organizations, as fraudulent activities can result in financial losses, reputational damage, regulatory penalties and loss of stakeholder confidence. The emergence of Artificial Intelligence (AI) has created new opportunities for improving fraud detection and prevention in accounting.
This research paper examines the role of Artificial Intelligence in fraud detection and prevention in accounting. The study focuses on the applications of machine learning, anomaly detection, natural language processing and predictive analytics in identifying fraudulent activities. It also discusses the benefits, limitations and ethical challenges associated with the use of AI. The study is based on secondary data collected from professional reports, research literature and authoritative publications. The findings indicate that AI can significantly strengthen fraud-risk monitoring by improving the speed, scale and consistency of transaction analysis. However, AI should not completely replace human judgment. Effective fraud prevention requires a combination of AI-based systems, internal controls, professional skepticism, employee awareness and human oversight.
Keywords: Artificial Intelligence, Accounting, Fraud Detection, Fraud Prevention, Machine Learning, Auditing, Financial Fraud, Anomaly Detection
Cite as
Ms. Kaleeswari Soundrapandi. (2026). Role of Artificial Intelligence in Fraud Detection and Prevention in Accounting. International Journal of Arts, Social Sciences and Humanities, 04(04), 532–539. https://doi.org/10.5281/zenodo.23474738
Rashmeet Kaur Ajmani and Dr. Rajesh Chouksey
Page 540 - 555
Abstract
The growing use of artificial intelligence (AI) in influencer marketing transformed the way fashion firms connect with consumers. This study examines the impact of AI influencer attractiveness, perceived expertise, and entertainment value on fashion purchase intention for generation Z consumers in Navi Mumbai. Data were acquired by employing a structured questionnaire from 200 generation Z respondents by purposive sampling technique. The proposed research model was examined using Partial Least Squares Structural Equation Modelling (PLS-SEM). Results show that AI influencer attractiveness, perceived expertise and entertainment value positively and significantly influence consumers’ fashion buy intention. Perceived expertise was the best predictor of purchase intention among the three antecedents (β = 0.405, t = 8.393, p < 0.001), followed by AI influencer attractiveness (β = 0.303, t = 5.208, p < 0.001) and entertainment value (β = 0.260, t = 5.451, p < 0.001). The structural model explained a large amount of variance in purchase intention (R2=0.758). This study adds to the increasing body of literature on AI influencer marketing by finding salient characteristics of AI influencers that influence consumers’ purchase intentions in the fashion industry. The findings have practical implications for fashion marketers and brand managers who wish to use AI influencers as a marketing communication tool to engage Generation Z customers and boost buy intentions.
Keywords: AI influencers, attractiveness, perceived expertise, entertainment value, purchase intention, Generation Z, fashion marketing, PLS-SEM.
Cite as
Rashmeet Kaur Ajmani, & Dr. Rajesh Chouksey. (2026). Understanding the Dynamics of AI Influencers: The Role of Attractiveness, Perceived Expertise, and Entertainment Value in Fashion Purchase Intention. International Journal of Arts, Social Sciences and Humanities, 04(04), 540–555. https://doi.org/10.5281/zenodo.23475009
Dr. Bhavna N Koli
Page 556 - 567
Abstract
The rapid growth of Artificial Intelligence (AI) and digital payment technologies has transformed the way financial transactions are conducted in India. This study examines AI awareness, trust, and digital payment usage among Generation Z in Navi Mumbai. A quantitative, descriptive and analytical research design was adopted, with primary data collected through a structured questionnaire from 189 respondents. AI awareness and trust were measured using a five-point Likert scale, and the data were analysed using descriptive statistics, the Kruskal–Wallis H test, and Spearman’s rank correlation.
Keywords: Artificial Intelligence, Digital Payments, Generation Z, AI Awareness, Trust, UPI, Navi Mumbai
Cite as
Dr. Bhavna N Koli. (2026). Artificial Intelligence and Digital Payment Adoption among Generation Z in Navi Mumbai: An Analysis of Awareness, Trust and Usage. International Journal of Arts, Social Sciences and Humanities, 04(04), 556–567. https://doi.org/10.5281/zenodo.23475344
Renu Vijay Verma
Page 568 - 575
Abstract
The rapid expansion of the digital economy has positioned artificial intelligence (AI) as both a transformative enabler of sustainability and a significant contributor to environmental degradation. This paper examines the dual role of AI within the digital economy, analyzing how intelligent automation, machine learning, and data-driven innovation can advance sustainable development while simultaneously creating new ecological burdens through energy-intensive computation, hardware proliferation, and electronic waste. Drawing on recent scholarship from the International Energy Agency, the Organisation for Economic Co-operation and Development, the United Nations Conference on Trade and Development, and peer-reviewed academic literature, this study synthesises evidence across three domains: the environmental costs of AI infrastructure, the potential of AI to accelerate progress toward the United Nations Sustainable Development Goals, and the emergence of Green AI as a disciplinary movement. The paper proposes a roadmap for reconciling innovation with sustainability, centred on energy-efficient algorithm design, circular economy integration, renewable energy procurement for data centres, and robust policy governance. The findings suggest that without deliberate intervention, the environmental footprint of AI will undermine the very sustainability objectives it promises to advance, making proactive stewardship an imperative for researchers, policymakers, and industry leaders alike.
Keywords: artificial intelligence, sustainability, digital economy, Green AI, circular economy, Sustainable Development Goals, environmental policy
Cite as
Renu Vijay Verma. (2026). Artificial Intelligence, Sustainability, and Innovation: A Roadmap for the Digital Economy. International Journal of Arts, Social Sciences and Humanities, 04(04), 568–575. https://doi.org/10.5281/zenodo.23475619
Dr. Saummya Rai
Page 576 - 591
Abstract
Artificial Intelligence (AI) is progressively reshaping the conceptual and operational foundations of finance and accounting. What began primarily as the automation of routine and rule-based processes has evolved into the application of machine learning, deep learning, natural language processing, predictive analytics and intelligent automation to increasingly complex financial and accounting decisions. Against this background, the present paper critically reviews the literature on the impact of AI on finance and accounting, with particular attention to financial forecasting, bankruptcy prediction, financial statement analysis, auditing, fraud detection, credit-risk assessment, investment analysis, organizational performance and professional transformation. The review demonstrates that AI has considerable potential to enhance computational efficiency, predictive capability, anomaly detection and decision support. However, the literature does not establish technological superiority as an automatic consequence of AI adoption. Rather, the effectiveness of AI is contingent upon data quality, model selection, organizational readiness, employee competencies, governance arrangements and the ability of professionals to interpret algorithmic outputs. An important theme emerging from the literature is the transition of accounting and finance from predominantly retrospective and transaction-oriented functions toward more predictive, analytical and strategically embedded roles. At the same time, increased reliance on algorithmic decision-making introduces concerns relating to explainability, bias, data privacy, cybersecurity, professional accountability and regulatory oversight. The review therefore argues that the long-term significance of AI lies less in the replacement of finance and accounting professionals than in the reconfiguration of professional work through human-AI complementarity. The paper concludes by identifying gaps concerning implementation, longitudinal organizational outcomes, explainable AI, professional competencies and emerging-economy contexts, thereby providing directions for future research.
Keywords: Artificial Intelligence, Finance, Accounting, Machine Learning, Auditing, Financial Forecasting, Fraud Detection, Risk Management, Professional Transformation, Human-AI Collaboration
Cite as
Dr. Saummya Rai. (2026). Artificial Intelligence in Finance and Accounting: A Critical Review of Applications, Transformative Impact, Challenges and Future Research Directions. International Journal of Arts, Social Sciences and Humanities, 04(04), 576–591. https://doi.org/10.5281/zenodo.23475967
Dr. Shobha Bennet Mathew
Page 592 - 608
Abstract
Artificial Intelligence (AI) is rapidly changing the way education is delivered, experienced, and managed. AI-powered tools such as intelligent tutoring systems, adaptive learning platforms, automated assessment systems, chatbots, and generative AI can provide personalized learning, immediate feedback, and support for teachers. Recent research generally indicates positive effects on student learning, although the results vary considerably according to the subject, educational level, duration of intervention, and way AI is used. This paper attempts to study the impact of AI on education
Cite as
Dr. Shobha Bennet Mathew. (2026). A study on the impact of AI on education. International Journal of Arts, Social Sciences and Humanities, 04(04), 592–608. https://doi.org/10.5281/zenodo.23476181
Nainesh Patel
Page 609 - 616
Abstract
Corporate Social Responsibility (CSR) has shifted from voluntary philanthropy to a core governance domain in which stakeholders demand verifiable evidence of social and environmental performance. Yet CSR reporting remains plagued by greenwashing, inconsistent metrics, and assurance practices that are largely point-in-time and self-interested. This paper examines how artificial intelligence (AI) can strengthen transparency and accountability in CSR initiatives. Drawing on a structured review of peer-reviewed literature and authoritative institutional reports published between 2011 and 2024, the paper develops a conceptual framework that maps four AI capabilities—natural language processing, machine-learning anomaly detection, blockchain-enabled distributed ledgers, and conversational AI—to four CSR mechanisms: dis-closure verification, real-time monitoring, traceability-based assurance, and participatory stake-holder engagement. The analysis argues that these mechanisms collectively reduce information asymmetry, harmonise divergent ESG ratings, and operationalise the EU Corporate Sustainability Reporting Directive (CSRD) and the Artificial Intelligence Act. The paper also identifies critical limitations, including the “black-box accountability paradox”, algorithmic bias, data-quality constraints, and a widening digital divide between multinational enterprises and small and medium-sized firms. It concludes that AI is a necessary but insufficient enabler: realising its transformative potential requires combined progress on data standards, third-party model audits, regulatory sandboxes, and meaningful stakeholder participation in AI governance.
Keywords: corporate social responsibility; artificial intelligence; transparency; accountability; ESG; greenwashing; blockchain; corporate governance.
Cite as
Nainesh Patel. (2026). The Role of Artificial Intelligence in Ensuring Transparency and Accountability in Corporate Social Responsibility Initiatives. International Journal of Arts, Social Sciences and Humanities, 04(04), 609–616. https://doi.org/10.5281/zenodo.23476711
Dr Subhash Ramachandran Nadar, Dr Shoba James, Ms Deepa Muthumani, Ms Jagatheeshwari Deivamuthu Nadar
Page 617 - 633
Abstract
Digital India programme over a decade shown an upward performance by setting a benchmark. It is a flagship initiatives of the Government of India. It has digitally empowering the society and digital economy. Digital economy is an economic activities enhanced with the digital transformation using the digital computing tools. It provided various opportunities in the Banking and Fintech sectors. In current scenario Artificial Intelligence (AI) playing a very significant pivotal role by integrating the technology in all the sectors. It is enhancing the opportunities for the service provider and customer.
Due to the rapid expansion of digital payment ecosystems, it has transformed the way how a consumers conduct their everyday financial transactions for purchasing their requirements either through online shopping i.e. e-commerce or visiting the store personal and using the digital banking or digital payment. In India, the digital payment especially using Unified Payments Interface (UPI) now has become a very important component of the digital financial ecosystem.
Alongside this growth, the payment service providers are progressively integrating with the Artificial Intelligence (AI) technology to personalise their customer experiences. This is providing an enhance features through transaction recommendations to the customers with amazing discount features or offers in the payments using the digital payments. It further enables the customers in identifying their spending insights and intelligent alerts and adaptive customer support.
This conceptual research proposes to examine the relationship between AI driven personalisation and customer trust in digital payment systems. The study propose to integrate from the perspectives of adopting the model Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) with the trust theory. The proposed model considers AI personalisation, perceived usefulness, perceived relevance, transparency as among the key explanatory variables, with customer trust as the principal dependent construct. Behavioural intention and continuance intention are considered as potential outcomes of trust.
The expected findings may provide very useful insight in the domain of digital banking, digital payment segment of the banks, fintech companies, and payment service providers to improve the trustworthy for the customer and responsible AI enabled payment ecosystems.
Keywords: Artificial Intelligence, AI-driven personalisation, digital payments, UPI, customer trust.
Cite as
Dr Subhash Ramachandran Nadar, Dr Shoba James, Ms Deepa Muthumani, & Ms Jagatheeshwari Deivamuthu Nadar. (2026). AI-Driven Personalisation in Digital Payments: Examining Its Impact on Customer Trust. International Journal of Arts, Social Sciences and Humanities, 04(04), 617–633. https://doi.org/10.5281/zenodo.23477465
Mr Shubham Rekha Shankar Pedamkar
Page 634 - 645
Abstract
In today’s technology-driven world, Artificial Intelligence (AI) is becoming an important part of higher education. College students are increasingly using AI tools such as ChatGPT, Google Gemini, Microsoft Copilot, Claude, and Perplexity AI for their academic work. These tools help students prepare assignments and presentations, understand difficult topics, find ideas, improve their writing, and complete academic tasks in less time. However, simply knowing how to use AI is not enough. Students also need to understand whether the information provided by AI is correct, how to use it responsibly, and what problems may arise from depending too much on these tools.
This study focuses on understanding the level of AI literacy among college students and its relationship with their academic performance. It will also study how familiar students are with generative AI tools, how often they use them, and for what academic purposes. The study will further examine the benefits students experience, the difficulties they face, and their understanding of issues such as academic honesty, originality, privacy, and responsible use of AI.
The study will use a mixed-methods research design, combining both quantitative and qualitative methods. Quantitative information will be collected through a structured questionnaire using Google Forms. Interviews and open-ended questions will be used to understand students’ personal experiences, opinions, and attitudes towards AI in education.
The study will include college students between 18 and 26 years of age from different academic backgrounds. The research will be limited to colleges in the Mumbai Metropolitan Region.
The study is expected to provide a clear picture of how AI literacy is connected with students’ academic performance. The findings may also help colleges and teachers plan AI literacy programmes and create practical guidelines for the proper use of AI in education. Overall, the study aims to encourage students to use AI as a helpful learning tool while maintaining independent thinking, originality, and academic responsibility.
Cite as
Mr Shubham Rekha Shankar Pedamkar. (2026). AI Literacy among College Students and Its Influence on Academic Performance: A Study of Generative AI Tools. International Journal of Arts, Social Sciences and Humanities, 04(04), 634–645. https://doi.org/10.5281/zenodo.23477788