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The adoption of financial technology (fintech) has the potential to make banking and financial services more accessible and convenient for all, but there are significant barriers preventing the adoption of fintech by street vendors and hawkers in India. This study aims to identify and analyse the barriers to fintech adoption using interpretive structural modelling (ISM). The research identified nine key barriers to fintech adoption, including perceived risk, lack of trust, lack of perceived benefit, social influence, lack of awareness, cash culture, lack of literacy, perceived ease of use, and lack of financial literacy. The study utilizes ISM to develop a hierarchy of these barriers and their interrelationships. The findings suggest that to promote the adoption of fintech, it is essential to build trust and awareness of fintech services through education and out-reach programs. This research provides a comprehensive understanding of the barriers to fintech adoption among street vendors and hawkers in India and offers insights into how these barriers can be overcomed. The study contributes to the development of strategies that can help promote the adoption of fintech among marginalized populations and advance financial inclusion in India.

The fintech segment is currently one of the most rapidly growing industries, attracting numerous investors who anticipate substantial returns in the future. Notably, not only individual retail investors but also mutual fund agencies are actively engaged in predicting stock prices within this sector to maximize their trading gains. The purpose of the study is to formulate stock forecasting models for top three Fintech Companies of India i.e., Policy Bazar, One 97 Communications Paytm Ltd., and Niyogin Ltd. Using Random Forest model with high-frequency data in Python. The literature review section also proves that this study is a novel piece of work as none of the existing research study focused on predicting stock prices of Fintech Companies of India using Random Forest model. The data is extracted from www.moneycontrol.com and www.kotaksecurities.com, for the period from 1st October, 2022–30 th September, 2023. The study deals about 293,280 data points i.e., 3 companies @ 97,760 each. It has been found that the forecasting model of random forest provides very successful results for prediction as the co-efficient of determination of all the selected companies is more than 95%.

AbstractThis research paper investigates the enablers of Sustainable Development Goals (SDG) leadership among Small and Medium Enterprises (SMEs) in South Asian nations using Interpretive Structural Modelling (ISM). The study focuses on identifying the factors that facilitate SDG leadership in SMEs, including shared vision, long-term perspective, co-creation, shared responsibility, empowering people, eco-centric values, creativity and innovation, bonding and trust, knowledge sharing, strategic adaption, and progressive culture. The research findings show that these factors are interrelated, and their integration can help SMEs to achieve sustainable development and become SDG leaders in their respective industries. The study contributes to the existing literature by providing insights into the enablers of SDG leadership among SMEs in South Asian nations and proposing a framework for their successful implementation. The proposed framework can be used as a guide for policymakers, managers, and entrepreneurs to promote sustainable development in their organizations and the region.

AbstractPromoting women’s economic participation and empowerment is essential for sustainable development in South Asian Countries. Studies have established that women entrepreneurs in South Asia face a noteworthy challenge with many lacking formal education, lack of entrepreneurship education, capital, skilled and trained manpower, management skills, networking, infrastructural support, fear of failure, gender discrimination, and loaded with family responsibilities. This research aims to evaluate the contextual relationships among identified key challenges and develop a hierarchical framework of key challenges for addressing barriers to promote women entrepreneurship in South Asian Countries. The primary study was conducted using Interpretive Structural Modelling on 31 women entrepreneurs from India, Nepal, Sri Lanka, Pakistan, and Bangladesh. The study proposed that multi-dimensional efforts are needed to ensure that women have equal access to economic opportunities and can fully participate in and contribute to the region’s economic development.

This paper estimates NIFTY index from Indian stock market by considering a cluster of MSCI European, Middle East and Asian stock market indices. In the forecasting process, we obtain group of independent variables to test its relative impact over dependent variable (NIFTY) considering a sample size of daily observations from January 2000 to December 2021 abstracted from Bloomberg. We run OLS regression, Quantile estimations with additional parameter of VIF and BKW. We found significant impact association with China (Asian index) and Saudi Arabia (Middle East index) during the forecasting process compared to rest of sample indices that exceed unexpectedly out of VIF limits. Further, we recorded strong association of independent variables despite of statistical significance (

The main objective of this empirical study is to investigate the impact of tax revenue on GDP dynamics at EU-28 level based on structural equation modeling (SEM). We applied structural equation modeling (SEM) which represents a multivariate statistical analysis technique used especially for analyzing structural relationships between measured variables and latent structures. Selected taxes are the following: environmental taxes, indirect taxes, social contributions, taxes on capital, taxes on labour, taxes on property, and direct taxes. The sample period includes a long time horizon during 2005-2017 for each member states of EU-28. Our empirical findings revealed the level of taxation exhibits an increased influence on GDP dynamics in case of EU-28 member states. JEL Codes - C3; E62; O47; O52

The future in the services industry belongs to Artificial Intelligence (AI) driven machines, which is a major source of worry for the job market in India. Over 50% of India’s GDP constitutes services, and it is a major source of employment for the skilled manpower of India. The research measures the impact of AI on service jobs in India based on qualitative parameters such as logical, natural, physical, and compassion; and finds which aspect serves the jobs better between machines and humans. The jobs taken over by AI are primarily at the task level more than the job level and for the basic tasks predominantly. The replacement starts with the basic tasks involved in providing service and then it grows to perform all the tasks involved in services. The research finds out that the logical aspects of the service will slowly reduce in the coming 5–10 years as AI will perform all the logic-related tasks leaving more emotional tasks such as compassion for humans. Finally, even these emotional-related tasks will be taken over by the AI which provides us with a very interesting combination of man and machine in the Indian scenario still threatening human employment.

Purpose – This article examines volatility spillovers, cross-market correlation, and comovements between selected developed and former communist emerging stock markets in the European Union. Modelling the behavioural dynamics of European stock markets represents a vital topic in a fascinating context, but also a current challenge of great interest. Research Methodology – We propose to estimate and model volatility using GARCH family models for selected European markets. We aim to explore volatility movement, presence of leverage effect/ asymmetry in selected financial markets. Findings – The econometric approach includes GARCH (1, 1) models for the sample period from 1, January 2000 to 12, July 2018. The empirical results revealed that exists a significant presence of volatility clustering in all selected financial markets except Poland and Croatia. The empirical analysis also indicates that both recent and past news generate a considerable impact on present volatility. Research limitations – Our empirical study has certain limitations regarding the relatively small number of only eight stock markets. Practical implications – It can provide a useful perspective for researchers, academics, investors, investment managers, decision-makers, and scientists. Originality/Value – The empirical analysis is focused on 8 European stock markets, which are classified as developed (Spain, UK, Germany, and France) and emerging (Poland, Hungary, Croatia, and Romania).

The main aim of this empirical study is to examine short and long run volatility movements based on a case study for Norwegian Stock Market, i.e. Oslo Stock Exchange. The econometric framework includes a series of statistical tests, ARIMA models and GARCH family models for the sample period from March 2013 to October 2021. The empirical results were influenced by the impact of COVID19 pandemic. This research paper also contributes to the existing literature regarding the influence of extreme events, such as COVID-19 pandemic on the behavior of developed stock markets, like Norwegian Stock Market.

The fundamental objective of our research study is to provide a critical analysis on Efficient Market Hypothesis (EMH), Adaptive Market Hypothesis (AMH) and Fractal Markets Hypothesis (FMH) considering their impact on stock markets behavior. Efficient Market Hypothesis is one of the pillars of modern finance and it is built on the paradigm that any publicly information can be considered as available for all financial investors, stock market participants or other actors in financial markets, and consequently asset prices always integrate and reflect all relevant information. Adaptive Market Hypothesis is based is a more recent theory whose theoretical architecture includes evolutionary principles. On the other hand, the Fractal Market Hypothesis is focused on the concept of the stock market liquidity, considering the fact that Efficient Market Hypothesis completely ignores this major aspect. Moreover, a liquid stock market represents a stable market which has significant implications at the investment level. Past financial evidence has shown that short-term price changes exhibit the obvious tendency to be more volatile compared to longterm price trends.

This study evaluates performance of Indian index considering NIFTY MIDCAP 50 index daily series returns. Autoregressive model EGARCH forecasts the volatility predictability and empirically analyze volatility pattern considering daily returns from NIFTY 50 index. The study tests presence of asymmetry in volatility transmitting patterns, Movement of higher positive and negative magnitude of shocks and fitness of the model. For this purpose data series considered from October 2007 to April 2021 consisting 3321 daily observations. This empirical study also attempts to capture the opportunity for investment returns and involvement of risk. Findings provide financial series movement, volatility sketches, summary of statistics and property of EGARCH model and fitness of series returns in EGARCH model.

The main aim of this paper is to investigate volatility spillover effects, the impact of past volatility on present market movements, the reaction to positive and negative news, among selected financial markets. The sample stock markets are geographically dispersed on different continents, respectively North America, Europe and Asia. We also investigate whether selected emerging stock markets capture the volatility patterns of developed stock markets located in the same region. The empirical analysis is focused on seven developed stock market indices, i.e. IBEX35 (Spain), DJIA (USA), FTSE100 (UK), TSX Composite (Canada), NIKKEI225 (Japan), DAX (Germany), CAC40 (France) and five emerging stock market indices, i.e. BET (Romania), WIG20 (Poland), BSE (India), SSE Composite (China) and BUX (Hungary) from January 2000 to June 2018. The econometric framework includes symmetric and asymmetric GARCH models i.e. EGARCH and GJR which are performed in order to capture asymmetric volatility clustering, interdependence, correlations, financial integration and leptokurtosis. Symmetric and asymmetric GARCH models revealed that all selected financial markets are highly volatile, including the presence of leverage effect. The stock markets in Hungary, USA, Germany, India and Canada exhibit high positive volatility after global financial crisis.

There are two kinds of strategies to control Non Performing Assets i.e. curative and preventive. The paper is an attempt to focus on the effectiveness of various preventive strategies in controlling NPA in future.For this study primary data have been collected from 82 branches out of 138 branches of Sagar District in Madhya Pradesh of India. The respondents are the branch managers or recovery officer of each branch.The primary data are related to the causes of NPA, actual usage of preventive measures and effectiveness of each preventive strategy. It highlights few new causes which are barely covered by the earlier researches. The study also represents the actual usage of various preventive measures along with the effectiveness of preventive measures in averting NPA to be occurred in future. The outcome of this study could provide a valuable insight about which strategy is more effective to prevent these stressed assets. Besides that, it could aware the banking authorities regarding the problems faced by the managers in using the preventive measures.

This research attempts to integrate the Virtual Try-On technology, an application of image integrative technology, and the technology acceptance framework to examine the influence of interactivity and customer involvement on the online shopping experience. It attempts to empirically test the conceptual model to establish how the virtual trial of apparel attributes influences conative responses towards an apparel retail website. The antecedents of the conative response (purchase intention) have been empirically validated for a sample size of 410 internet users in India specifically of the millennial cluster. Result reveals the importance of confidence in apparel fit, utilitarian value, and hedonic value in strengthening the purchase intention of an online shopper.Body esteem is also a significant antecedent to purchase intention, while customers shop online. This research endeavor affirms the role of a virtual trial in strengthening the purchase intention in the virtual market space. The outcomes of this study add to the body of knowledge about the efficacy of Image Integrative Technology.

Liberalization of the economy has cemented the open road for many to become entrepreneurs. The possibilities created by today’s comprehensive awareness together with the unfettering of indigenous ventures, has laid a fertile ground for entrepreneurship to thrive on in India. There are several challenges faced by entrepreneurs today and study reveals that most of challenges are because of ethical issues in growing entrepreneurship. The main objective of this research paper is to investigate the entrepreneurship and business developments and their intrinsic relationship with the concept of ethics. Currently, the linkage between entrepreneurship and business ethics is a growing challenge with multidimensional implications.

Purpose: The purpose of this paper is to formulate a multiple regression model by considering those factors which are positively affecting the growth of Micro, Small and Medium Enterprises (MSMEs) in India. Methodology: The data used in this study are primary in nature which are collected through questionnaire method. The questionnaire involves the nature and size of business and opinion of the owners and managers of 454 MSMEs in Katihar district, a semi-urban area of Bihar in India, regarding the various favourable factors of digital banking on a Likert scale of 1 to 10. Findings: The findings of the study suggest the various favourable factors of digital banking with significant coefficients i.e. Level of Easiness in accepting payments, Level of Easiness in making payments, Level of Easiness in Managing the expenditure of Business, Level of Time Saving, and Level of Check on Misappropriation or Theft of Cash are contributing towards the growth of MSMEs in India. Research Implications: This research will be helpful to the bank managers and policy makers to encourage the MSMEs of semi-urban areas to use digital banking by stressing on only significant favourable factors and also to take necessary steps so that the MSMEs could reap the full benefits of digital banking. The study could give a new insight regarding the extent of contribution of digital banking towards the growth of MSMEs in rural and semi-urban area.

Existence of short-term momentum effect is a widely disputed topic. This paper aims to solve the dispute and examine the presence of short-term momentum effect in the stock market of Hong Kong. Empirical results of 16 monthly price momentum investment strategies suggest that short term momentum effect has been found in Hong Kong’s stock market and it is overwhelmingly strong. Future research should focus on investigating momentum effect using weekly and daily price momentum strategies.