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The financial technology (FinTech) era has brought a revolutionary change in the financial sector’s customer experiences at the national and global levels. The importance of artificial intelligence (AI) in the context of FinTech services for enriching customer experiences has become a new norm in this modern era of technological advancement. So, it becomes crucial to understand the customer’s perspective. The current research ranks the factors and sub-factors influencing customers’ perceptions of AI-based FinTech services.

The sample size for this study was decided to be 970 respondents from four Indian cities: Mumbai, Delhi, Kolkata and Chennai. The Fuzzy-AHP technique was used to identify the primary factors and sub-factors influencing customers’ experiences with AI-enabled finance services. The factors considered in the study were service quality, trust commitment, personalization, perceived convenience, relationship commitment, perceived sacrifice, subjective norms, perceived usefulness, attitude and vulnerability. The current research is both empirical and descriptive.

The study’s three top factors are service quality, perceived usefulness and perceived convenience, all of which have a significant impact on customers’ experience with AI-enabled FinTech services discussing sub-criteria three primary criteria for customers’ experience for FinTech services include: “Using FinTech would increase my effectiveness in managing a portfolio (A2)”, “My peer groups and friends have an impact on using FinTech services (SN3)” and “Using FinTech would increase my efficacy in administering portfolio (PU2)”.

The current study is limited to four Indian cities, with 10 factors to understand customers’ preferences in FinTech. Further research can focus on other dimensions like perceived ease of use, familiarity, etc. Future studies can have a broader view of different geographical locations and consider new tech to understand customer perceptions better.

The study’s findings will significantly assist businesses in determining the primary aspects influencing customers’ experiences with AI-enabled financial services. As a result, they will develop strategies and policies to entice clients to use AI-powered FinTech services.

Existing AI research investigated several vital topics in the context of FinTech services. On the other hand, the current study ranked the criteria in understanding customer experiences. The research will substantially assist marketers, business houses, academicians and practitioners in understanding essential facets influencing customer experience and contribute significantly to the literature.

Organic food market has grown rapidly on a global level and so is the interest of customers. The present paper ranks the factors and sub-criteria which are taken into consideration while making organic purchase decisions resulting in understanding the behaviour of consumers.

The present paper considered a sample of 550 respondents in the area of Punjab. Fuzzy AHP technique was applied to understand the key factors and sub-criteria which play a major role in organic food purchase decisions. The paper is empirical and descriptive in nature. The factors considered for the study include price, consumer knowledge, trust, attitude, behavioural intentions, subjective norms, perceived personal relevance and perceived consumer effectiveness.

The three major influential factors include price, trust and attitude ranked in the same order of preference which majorly affects the purchase decisions and talking about sub-criteria the three major criteria to purchase organic food include: “Price plays a significant role in purchase decisions (P2)”, “Organic food keeps me fit and healthy (A1)” and “Organic food intake makes me feel energetic (A2)”.

The present paper is limited to the area of Punjab and majorly eight factors have been taken into consideration. Further research can be explored on broader geographical and cultural areas with new dimensions in criteria and sub-criteria.

The findings of this paper will surely help the marketers to understand the behavioural intentions and preferences of the customers. Accordingly, they will strategize the policies to convert organic food market into a niche market with a high growth rate.

The existing literature explored various key factors. However, the present study comes up with ranking to the factors according to their priority in purchase decisions. This will definitely help marketers, business houses, practitioners and academicians about the key factors which affect purchase decisions, and it will surely add incredible knowledge into the existing database.

In today's competitive environment, sustainability is talked out in every sphere of life. Sustainability is a key to stability and for that roots are being focused by incorporating sustainability in higher education. The basic purpose of this paper is to prioritize the sustainability drivers in the higher education system. This research will provide fruitful insight into the sustainability drivers in the higher education system to the education industry and policymakers.

The present research is conducted on the 400 students studying in four major universities in the state of Punjab. Fuzzy analytical hierarchy process was applied to prioritize the sustainability drivers in the higher education system. The primary factors considered for the present study include social-people (social responsibility), environmental-planet (sustainable environmental practices) and financial-profit (economic value created).

The most influential criteria were environmental-planet (sustainable environmental practices) and social-people (social responsibility). The five most influential subcriteria were “Student engagement in eco co-curricular activities (C21)”, Energy efficiency measures (C23)”, “The HEI as a job driver in the city (C11)”, “Total direct energy consumption (C31)” and “Support from the HEI for local initiatives and help in growing the sustainability of the community or region (C12)”.

Although the sample survey conducted in this study was focused on a small sample selected from the state of Punjab which genuinely represented the total population, it is still considered as a limitation for the present study.

The outcome of this research provides policymakers with a better understanding of the sustainability drivers in higher education. This will further help them toward achieving the aim of sustainability.

The present research is based on the available literature on sustainability and the results of the study would add value to the existing knowledge base.

The Indian leather industry contributes to economic growth at a significant environmental cost. Due to the rising global demand for sustainable leather products, promoting efficient input utilisation has become vital. This study measures input efficiency and its determinants for leather industry in order for it to improve its future performance.

In the first stage, bootstrap data envelopment analysis (DEA) approach is used for measuring efficiency and analysing firms' differences based on their geographical location, organisational structures, urban-rural location and sub-industrial groups. A second stage regression examines efficiency determinants using size, age, skill and capital-labour intensity as the explanatory variables.

Efficiency result shows a significant potential of minimising inputs by 47% provided the firms adopt best practices. West Bengal firms, urban located firms, individual and proprietorship owned firms and leather consumer goods firms are found to be relatively efficient to their counterparts. Size, skilled managerial staff and labour-intensive firms positively affect efficiency.

Construction of well-connected roads for accessing urban retail markets and provision of reliable electricity would improve efficiency of rural firms. Small-scale enterprises have a larger share in Indian leather industry; therefore, policy should focus on enhancing the firms' scale and investing in training facilities to skill employed labour for ensuring optimal use of inputs.

Previous studies on the leather industry have used the conventional DEA efficiency measurement approach. This study uses DEA bootstrapping model for robust efficiency estimates and provides consistent inferences about the determinants.