Purpose : This study aimed to investigate the trends and sustainability of public debt in India from 1980 to 2022. Design/Methodology/Approach : A moving average and graphical approach were employed to capture the trend in India’s internal, external, and total debt. The fiscal reaction function (FRF) and indicators-based approach were applied to examine the sustainability of public debt under the autoregressive distributed lag model. Findings : The graphical and moving average results indicated that India’s overall public debt-to-GDP ratio increased over the study period. A significant decline was observed in the external debt-to-GDP ratio, particularly after the 1991 economic reforms. In contrast, the internal debt-to-GDP ratio indicated an upward trend. The indicator-based approaches showed mixed results. The FRF results indicated that during the study period, public debt in India remained sustainable due to a favorable interest-growth differential rather than a positive primary balance. Practical Implications : The findings of the present study improved the understanding of public debt behavior and sustainability concerns in India. Originality/Value : This study contributed to the literature by conducting trend analysis of public debt and debt components and sustainability, examined through an indicator-based approach and the FRF. It made a valuable contribution to the literature on public debt trends and sustainability, using a theoretical and empirical methodological framework. Additionally, it explored how macroeconomic variables over the five phases of the study period determined debt sustainability in India.
Purpose : In India, a small group of equity fund managers managed a large, fast-growing corpus of household savings. When these managers herded, prices drifted from value, and the market turned fragile. The Indian regulator has periodically reset fund rules, which forced managers to take identical trades. This raised a key question: was herding rule-enforced or manager-chosen? Design/Methodology/Approach : To separate the two, this study used the October 2017 rule change, which redefined market-capitalization categories and scheme mandates, forcing many managers to rebuild portfolios through 2018. Herding was measured from monthly holdings of 372 open-ended equity mutual fund schemes, 2013 to 2024, using a bias-corrected structural measure. Each trade was then split into flow-driven and discretionary parts. Findings : Fund managers herded heavily, at about 18% overall. Though the rule change forced nearly ` 24,000 crore of buying, mostly in mid-caps, it did not raise herding. Herding was instead mostly a matter of choice: 17.5% in chosen trades against only 6.6% in flow-driven trades. Large-cap-mandated mutual funds held almost the same stocks, with an average overlap of 0.70. Practical Implications : Herding arose from how mutual fund mandates were designed and how managers were rewarded, not from regulatory rule changes. Softening rule changes did not reduce it. Fund managers who held large-cap mandated schemes were less diversified than they appeared. Originality/Value : This study is a novel attempt to separate forced from chosen herding using portfolio holdings of Indian equity fund managers.
Purpose : Financial Technology (FinTech) has transformed the financial landscape by making financial services more accessible, transparent, and efficient, while environmental, social, and governance (ESG) investing has become an integral part of sustainable economic growth. Although digital financial platforms grew rapidly, the impact of technological, institutional, and behavioral factors on ESG investment decisions had received little empirical attention, especially in emerging markets. This study examined the effects of FinTech Ease, Government Support, AI Impact, and Trust on perceived ESG value among retail investors in India. Design/Methodology/Approach : A quantitative, cross-sectional research design was adopted. Data were collected from 323 retail investors, FinTech users, mutual fund investors, and finance professionals through a structured online questionnaire. Partial Least Squares Structural Equation Modeling (PLS-SEM) in SmartPLS 4.0 was used to test the proposed research model. Findings : The results indicated that FinTech ease, government support, and AI impact all had a significant positive effect on trust, and that FinTech ease, government support, AI impact, and trust had a significant positive effect on ESG value, with FinTech ease showing the strongest total impact. Practical Implications : The study offered actionable recommendations for FinTech companies, policymakers, and ESG investment platforms to strengthen digital investment ecosystems, improve transparency, foster investor confidence, and encourage broader sustainable-investing adoption in emerging markets. Originality/Value : The study was among the first to integrate the technology acceptance model and stakeholder theory into a single framework explaining how technological ease, institutional support, and AI-enabled decision support jointly shaped ESG value through investor trust, using empirical evidence from a fast-growing digital financial market that had been largely overlooked in prior sustainable-finance research.
Purpose : This study examined the impact of financial knowledge (FK), attitude, and behavior on financial planning practice adoption among higher education teachers. Research Design/Methodology/Approach : Data were collected from 246 faculty members across different academic disciplines for the proposed issue of study, and their responses were considered the main data for analysis. For hypothesis testing of the proposed study, partial least squares structural equation modeling was executed to explore the different correlations among the variables. The study is guided by the theory of planned behavior for understanding financial planning practices, with specific emphasis on the role of financial attitudes (FA) and behavior in shaping financial decision-making. Findings : The results showed that financial behavior (FB) and knowledge are the main determinants of financial planning practices. However, FA acts as a mediator in the sequential relationship between FK and financial planning practices through FB. Practical Implications : The results of this study guided policymakers and educational institutions in designing faculty development programs and training programs on financial literacy. Originality/Value : This study contributed to the financial literacy literature by focusing on the relatively underexplored context of higher education teachers in India. It examined the interactive effects of FK, attitude, and behavior on financial planning practices and empirically validated the role of FA in this relationship as sequential mediation.
Purpose : The study examined whether behavioral biases, risk perceptions, and investment framing differentiated Indian investors across levels of portfolio exposure to blockchain-based digital assets. Design/Methodology/Approach : Data were collected from 1,139 Indian investors through an online survey. A partial least squares structural equation modeling approach was used to assess the measurement properties of five behavioral-bias constructs. Multinomial logistic regression was then employed to examine differences across three exposure categories: less than 5%, 5–10%, and more than 10%. Findings : Overconfidence was positively associated with higher exposure, whereas reliance on experts was negatively associated with the highest exposure category. Perceived risk of government shutdown was negatively associated with exposure. Investors who did not view blockchain-based digital assets as lottery-like were more likely to report higher exposure. Practical Implications : The findings suggested that financial advisors, platforms, and regulators should account for behavioral biases, perceived regulatory risks, and investment framing when designing investor education and risk communication. Originality/value : The study extended research on digital-asset investment by examining behavioral and perceptual factors across portfolio-exposure levels rather than treating investment as a binary outcome or general intention.
Purpose : In this study, we investigated whether a geopolitical risk index (GRI) was a better predictor of Indian commodity prices than traditional econometric models and whether machine-learning models could have improved forecasting performance relative to conventional econometric approaches. Methodology : In India, the daily wholesale prices of cotton and turmeric were analyzed using the Caldara and Iacoviello global GRI. Risk transmission was quantified using measures of Granger causality and connectedness analysis. Three machine-learning architectures were estimated in both plain and risk-augmented variants and compared with linear econometric benchmarks. The accuracy of the forecasts was evaluated on a held-out partition and was also tested using a rolling-origin cross-validation approach. Findings : Geopolitical risk was a significant predictor of turmeric prices, but not cotton prices, which is perhaps understandable given that cotton benefits from government price-support mechanisms. For both commodities, the models based on recurrent neural networks were superior to the gradient boosting and linear benchmark models, and adding a geopolitical risk variable enhanced the accuracy of forecasts in almost all models evaluated. Practical Implications : There is a potential for the inclusion of geopolitical risk indicators in commodity price monitoring systems in emerging markets, with special emphasis on commodities that trade in open, unregulated markets where the predictive relationship was clearly established. Originality : The study provided evidence of geopolitical risk transmission to structurally contrasting Indian agricultural commodities using daily-frequency data and compared the forecasting performance of machine-learning and econometric models in the context of risk augmentation.
Purpose : The current research aimed to investigate the relationship between estimated green-investment requirements and projected carbon-emission reductions across five micro, small, and medium enterprise subsectors in India. Design/Methodology/Approach : This research employed secondary data analysis to investigate estimated green-investment requirements and projected carbon-emission reductions in five micro, small, and medium enterprise subsectors: textile, paper and pulp, foundry, forging, and steel rerolling. Microsoft Excel was used to perform Pearson correlation and significance testing. Sectoral comparison was carried out based on three key variables: estimated green-investment requirements, projected carbon-emission reductions, and energy efficiency potential. Findings : The study found a strong positive association between estimated green-investment requirements and projected carbon-emission reductions (r = 0.864). However, the relationship was not statistically significant at the 5% level (p = 0.059). Sectoral comparison also showed differences among the subsectors in projected carbon-emission reductions, estimated green-investment requirements, and energy-efficiency potential. Given the limited five-subsector dataset, the findings were interpreted as exploratory. Further longitudinal and empirical research was considered necessary to determine whether sectors with higher investment requirements also exhibited greater projected emission-reduction potential. Practical Implications : The results indicated that green-transition planning needed to consider the differences in sectoral investment requirements, emission-reduction potential, and energy-efficiency opportunities rather than relying solely upon the level of investment. Originality/Value : The study provided an exploratory sector-level comparison of estimated green-investment requirements, projected carbon-emission reductions, and energy-efficiency potential and offered a differentiated basis for green-finance prioritization across energy-intensive micro, small, and medium enterprise subsectors.
Purpose : This study examined whether India’s short-run Phillips Curve was a stable, structural relationship or an episodic one over 2010–2025, extending prior Indian evidence by 14 years through the flexible inflation targeting era across four inflation channels. Design/Methodology/Approach : Using quarterly consumer price data (n = 62), the study estimated a full-sample ordinary least squares regression with a Hodrick-Prescott-filtered output gap and the real effective exchange rate as a supply-shock proxy, then tested long-run cointegration through bounds testing, structural stability through Chow tests, and time variation through rolling twelve-quarter regressions across 51 windows. Findings : Inflation inertia dominated (β1 = 0.812, p < 0.001, R² = 0.772), and the output gap remained insignificant on average (β2 = 0.002, p = 0.949) and significant in only three of 51 rolling windows. The bounds test (F = 1.503) ruled out long-run cointegration, and policy transmission was significant only in the post-targeting period (β4 = −2.860, p < 0.05), with all Chow tests confirming stability. Practical Implications : Rate tightening worked best when the output gap was clearly positive, and was counterproductive against food or fuel shocks. Anchoring the credibility of the inflation target remained the most reliable long-run disinflation tool. Originality/Value : Among the first studies to apply rolling-window regression to India's quarterly Phillips Curve, the paper showed the relationship to be episodic rather than absent, with implications for central bank policy design.
Purpose : The purpose of this study was to examine the role of strategic green marketing practices in the Indian banking sector in minimizing adverse environmental impact by promoting green technology financing and implementing sustainable green banking practices. The study specifically examined how green social responsibility (GSR), green processes, and green products influenced green brand perception and green brand equity (GBE) among Indian bank customers. Design/Methodology/Approach : The study adopted a quantitative research approach supported by an extensive review of relevant literature to develop a conceptual research model. Primary data were collected through a structured questionnaire administered to 330 Indian bank account holders. Factor analysis and structural equation modeling (SEM) were employed to examine the relationship between key green marketing dimensions, GSR, green processes (GP), and green products, and their influence on green brand image (GBI) and GBE. Findings : Green marketing dimensions significantly enhanced customer perceptions of GBI and positively influenced GBE in the Indian banking sector. Among the three dimensions, green products emerged as the strongest contributor, followed by GP and GSR. The results indicated that strategic green marketing practices indirectly contributed to minimizing environmental degradation by encouraging environmentally responsible banking operations and supporting sustainability-oriented financial behavior. Practical Implications : The findings have important implications for banking practitioners and policymakers by emphasizing the need to integrate green marketing elements into strategic decision-making processes. Bank managers could strengthen the GBI, enhance customer trust, and improve brand equity by offering environmentally responsible products, implementing sustainable internal processes, and pursuing social responsibility initiatives. The study also offered guidance for promoting sustainable growth, reducing environmental impact, and enhancing social well-being through effective green banking strategies. Originality/Value : This study contributes to the growing literature on green banking by offering a novel perspective on how green marketing strategies in the banking sector indirectly support environmental sustainability and reduce the adverse effects of global warming. Unlike previous studies, it integrates GSR, GPs, and green products to explain green brand perception and equity in the Indian banking context, while highlighting current sustainability initiatives adopted by banks to promote long-term environmental and social welfare.
Purpose : The success of circular business models depends on understanding the drivers of circular consumption and the consumer’s willingness to participate, yet the foundations of individual decision-making remain overlooked. This study aimed to evaluate the key drivers of circular consumption and how the interaction of psychological, economic, and infrastructural factors affected the circular practices among youth. Design/Methodology/Approach : This study adopted a multi-stage methodological framework to evaluate the key drivers of circular consumption. A multi-stage causal research design was used to evaluate the Indian Youth’s willingness to participate in the circular economy. The Delphi technique, rough stepwise weight assessment ratio analysis, and multi-criteria decision-making approach were used to assign relative weights to the nine important re-activities in the consumption process. Survey data from 234 respondents were subjected to principal component analysis for data reduction, followed by the development of a binomial logistic regression model to evaluate how these factor scores predict the willingness to participate in the circular economy. Findings : The econometric model results indicated that the strongest significant positive driver of participation was responsible consumption, increasing the odds of willingness to participate by approximately 21.2% (B = 0.192, Exp (B) = 1.212, p < 0.001). Conversely, affordability/market-based access acted as a statistically significant financial hurdle that reduced the likelihood of participation (B = –0.645, p = 0.020), and philanthropy/altruistic relinquishment emerged as a distinct behavioral trait from principal component analysis but was not found to be a statistically significant driver in the final decision-making process (p = 0.202). Practical Implications : Businesses should replace vague corporate social responsibility messaging with campaigns highlighting trackable resource savings from repairing, reselling, or reusing. Gamified apps that visualize carbon reduction or waste aversion can further activate the moral norms of youth consumers. For bridging the affordability gap, firms should replace “premium” positioning with flexible, low-cost “pay-per-use” models and offer financial incentives for product returns to foster loyalty. Additionally, government policy must shift from mere awareness campaigns to structural interventions, such as subsidizing repair infrastructure, enforcing “right to repair” frameworks, and deploying localized incentives. Originality/Value : Unlike prior research focusing on business-to-business supply chains, this work provided a holistic, consumer-centric framework that bridged multi-criteria prioritization and predictive econometric modeling to disentangle the trade-offs between environmental values and pragmatic constraints.
Purpose : The objective of this research was to explore critical determinants of mobile banking (M-banking) adoption for achieving financial inclusion in rural India. Design/Methodology/Approach : An integrated research model was assessed through a two-step CB-SEM to explore the key determinants of M-banking adoption. A total of 404 data samples were collected from Andhra Pradesh (130), Karnataka (140), and Telangana (134). Findings : The results established a positive impact of usefulness, ease of use, self-efficacy, and social influence on M-banking adoption, and a negative impact of cost and risk on M-banking adoption. Self-efficacy had a positive impact on usefulness and ease of use; whereas, social influence had a negative impact on risk. Practical Implications : The study contributed to technology and behavioral research. Furthermore, it provides a better understanding of key factors of M-banking adoption among rural populations. Originality : This study used a unique research model that integrated the critical variables of TAM, UTAUT, DOI, and TPB. The research model explained 79.4% of the variation in m-banking adoption.
Purpose : This study investigated the impact of artificial intelligence (AI) adoption on the price dynamics of AI-driven stocks. Within the Nifty India Digital Index, this study focused on stock prices of Tata Elxsi, Infosys, and Persistent Systems owing to their strategic commitment to AI through considerable investments in proprietary platforms, intellectual platforms, and other AI-driven revenue streams. Methodology : Employing daily closing price data from April 1, 2022, to March 31, 2025, this study applied a suitable econometric time-series model to capture the heterogeneous statistical properties of stock prices. The best model was selected based on rigorous diagnostic testing and forecast performance evaluation. Findings : The study found that AI-driven stocks exhibited different statistical properties, requiring customized forecasting models. The EGARCH (1,1) model best predicted Tata Elxsi stock prices, while ARFIMA models provided the best forecasts for Infosys and Persistent Systems stock prices. Implications : These findings offered different perspectives to investors in framing stock-specific forecasting strategies while analyzing AI-driven stocks. Originality : This study is the first of its kind, as it emphasizes the role of tailored econometric modeling in prediction accuracy while amalgamating AI-driven market context with time series forecasting in the Indian digital economy.
Purpose : This study examined the impact of key macroeconomic factors (liquidity, gross domestic product (GDP), real effective exchange rate (REER), consumer price inflation (CPI), and real interest rate) on mergers and acquisitions (M&As) volumes within the QUAD countries (Australia, India, Japan, and the United States), evaluating domestic, inbound, and outbound M&As segments. Methodology : The research employed a negative binomial fixed-effects panel regression model to analyze M&As activity for each QUAD country individually and as a collective bloc. Findings : The study revealed that foreign direct investment (FDI) consistently influenced all types of M&As activities across the QUAD bloc. Liquidity and REER exhibited varying effects depending on the M&As segment, while GDP, inflation, and interest rates showed moderate and segment-specific impacts, underlining the complex interaction between macroeconomic conditions and cross-border investments. Practical Implications : This research contributed to the limited literature on M&As by focusing on the strategic role of macroeconomic stability in the QUAD countries. The findings provided valuable guidance for policymakers and firms involved in strategic cross-border transactions within the Indo-Pacific region. Originality : Unlike prior research focusing on BRICS economies, bilateral FDI, or target-country factors, this study provided the first systematic evidence on how macroeconomic factors shaped M&A activity in QUAD countries.
Purpose : This study investigated the validity of the Arbitrage Pricing Theory (APT) in the Indian capital market and compared its explanatory power with the capital asset pricing model (CAPM). Methodology : Using monthly data spanning seven years (2017–2023), the study analyzed excess returns of 15 large-cap stocks and the NIFTY index. Five macroeconomic factors: GDP growth rate, Index of Industrial Production (IIP), inflation, interest rate, and exchange rate, were selected from the literature and validated using the principal component analysis (PCA). A multivariate regression framework was employed to assess the explanatory power of APT, while CAPM was tested using market returns as the sole factor. The analysis focused on three key statistical indicators: the R2 value to assess the model’s explanatory strength, the F-test for overall model significance, and p-values for the statistical significance of each independent variable. Findings : The findings indicated that APT demonstrated weak explanatory power, with consistently low R2 values and statistically insignificant coefficients for most macroeconomic variables. In contrast, CAPM exhibited relatively higher explanatory strength across all securities. Practical Implications : The study contributed to the limited and inconclusive evidence on APT in the Indian context by providing a direct empirical comparison with CAPM. The results indicated the failure of macroeconomic factors to adequately capture stock return variations in India, suggesting limited practical applicability of APT. Originality : The study focused on the application of APT to the Indian market using a multivariate framework, supported by PCA-based factor validation.
Purpose : To address the time-varying integration of the Indian stock market and to understand the true nature of domestic returns and their alignment with the global market. If misaligned, what could be the causes? Methodology : The International Capital Asset Pricing Model framework proposed by Levine and Zervos was used to estimate the Market Integration Index. The index was further extended to a time-varying parameter using the Kalman Filter. Findings : Domestic returns consistently outperformed global returns. It was found that 60% of the return was aligned to domestic factors; thus, only 40% was sensitive to global factors. The Kalman filter showed that the Indian stock market was deviating from the global market between 2012 and 2025, with two rare instances of co-movement. Practical Implication : The positive and significant α0, together with β0 > 1, suggested the existence of a low-beta anomaly, implying that investors can earn higher returns at relatively lower risk, which enhances the attractiveness of the Indian stock market. Originality : Numerous studies have investigated the integration of the Indian stock market with global markets; however, they have used fixed coefficient models, which did not capture the true dynamics of market integration, which is time-varying in nature. Hence, the Kalman filter, a state-space technique that allows parameters to vary across time, was used with the international capital asset pricing model to unveil the same. The study used the latest period from January 2012 to July 2025; hence, it would meet the needs of the current crop of investors.
Purpose : The objective of this study was to identify the non-linear predictors of mispricing in India. Furthermore, the study evaluated the variation in overpriced and underpriced issues using the spline analysis in India. Design/Methodology/Approach : The study is based on 571 issuers that went public between 2017 and 2025, as derived from CMIE Prowess. It employed an advanced form of spline regression, restricted cubic spline (or natural spline), to examine the non-linear predictors of mispricing. Findings : Usually studied based on linearity, mispricing in the Indian primary market was predicted by issue price and premium percentage as major determinants on the basis of polynomial evaluations of spline regression. These variables are not identified with linear modeling. The study further illustrated that there was a significant variation in overpriced and underpriced issuers, with most of the issuers having an underpriced listing. Originality/Value : The novelty of this study lies in its focus on non-linear predictors of IPO mispricing using Spline regression analysis. Linearity assumes a constant linear relation among study variables, but splines have substantial potential to predict the information about fluctuating relations among variables, depicting a clearer picture closer to reality.
Purpose : The article highlighted the challenges faced by the depositors and borrowers in India to compare the interest rates across banks and other institutions. Methodology : Three major Indian banks, State Bank of India (SBI), HDFC Bank, and ICICI Bank, for comparison with United States banks, SBI California and Royal Bank of Canada (RBC), were selected. Only three banks from India were selected because all other banks similarly quoted the interest rates. Findings : It was found that the banks in India quoted the interest rates in nominal terms without taking into account the effect of compounding, processing fees, and other charges, which makes the interest rates difficult to compare. Practical Implications : The paper emphasized that banks should quote the effective interest rates (EIR) instead of nominal interest rates. For the interest of the depositors and borrowers, the RBI should intervene and issue guidelines in this regard. Originality : The paper tried to fill the research gap and suggested that the banks and other institutions involved in lending and borrowing should quote EIR along with nominal interest rates.
Purpose : The growing adoption of artificial intelligence (AI) in the financial services sector has led to the emergence of robo-advisory platforms that offer automated investment advice. This study examined the factors affecting users’ intention to invest in AI-based platforms. Design/Methodology : The study was based on the technology acceptance model (TAM), as well as behavioral finance and consumer psychology concepts, making it concentrate on five variables, namely: Perceived Algorithmic Accuracy (PAA), Cognitive Absorption (CA), Emotional Trust (ET), Perceived Value Co-Creation (PVC), and Artificial Intelligence Usage (AIU). Emotional trust was used as a mediator, and AI Usage as a moderator. The survey was conducted through online questionnaires on 445 active financial investors. PLS-SEM was used to analyze the responses. Findings : The results indicated that PAA, CA, ET, and PVC significantly influenced investment intention. It was established that ET partially mediated the relationship between PAA and investment intention. Furthermore, AIU exerted a strong mediating effect on CA on investment intention, but this mediation was not notable in ET and PVC. These findings indicated that emotional and cognitive involvement, as well as trust in the use of algorithms, were significant factors in user behavior. Practical Implications : The research provided first-hand insights into how financial technology providers could improve trust, user engagement, and platform engineering. Originality/Value : This study integrated technical, psychological, and participatory elements into a single model of robo-advisor adoption, with emotional trust and cognitive absorption as key determinants of investment intention.
Purpose : The present paper investigated the growth of shareholder value and the factors influencing shareholder value in Indian automobile companies, focusing on perceptions of value-based and accounting metrics. The automobile sector was an economic engine in India. Profit and shareholder wealth were concentrated along a straight line. The current study focused on providing empirical evidence on the influencing factors of shareholder value. Methodology : A total of eight independent variables (return on assets, return on equity, net profit, current ratio, share price, economic value added (EVA), refined EVA (REVA), and market value added) and shareholder value as the dependent variable were amalgamated in the study. The sources used in the research were quantitative data collected from the companies’ annual reports. The historical data were assessed from 2010 – 2025. EViews software was used for the analysis. Findings : The findings showed that SP, EVA, market value added (MVA), and refined economic value added (REVA) had a significant impact on the shareholder value of the assessed companies. The study concluded that calculating and examining value-based metrics was important in understanding and enhancing the trend in shareholder value. Practical Implications : The results recommended that financial regulators can encourage companies to adopt value-based metrics, which are essential for improving shareholder value and ensuring long-term corporate sustainability. Originality/Value : This research provided a novel viewpoint on shareholder value with value-based metrics. Unlike prior research on shareholder return, the current studies incorporated both accounting and value-based metrics.
Purpose : The purpose of this study was to examine the role of artificial intelligence (AI) in strengthening the breach detection mechanisms and addressing emerging challenges in password management (PM). Design/Methodology/Approach : The study adopted a quantitative research design and used primary data from 413 respondents from the National Capital Region, India. The partial least squares-structural equation modeling (Smart PLS-SEM) version 4.1.1.2 method was used to examine the moderating effect of AI on PM. Findings : The study found that AI impacted password generation, automated threat detection, password reuse detection, user experience, password cracking detection, and password recovery, which led to PM. Practical Implications : The study highlighted the need for organizations to integrate AI-enabled security solutions to proactively mitigate password-related cyber threats and data breaches. Originality/Value : This paper contributed to the cybersecurity literature by emphasizing AI as a timely and essential solution for modern breach detection and effective PM in an evolving digital environment.
