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The objective of this research is to identify and prioritize the critical factors that influence the adoption of blockchain technology within the banking sector.

A well-known theoretical framework, the “Technology Organization Environment (TOE),” was chosen to analyze what criteria and sub-criteria affect blockchain adoption in the banking sector after a thorough assessment of the prior literature. Following that, 3 evaluation criteria and 14 sub-criteria were selected and verified using expert opinion. A survey design was created, and data for the study has been collected from various information technology (IT) managers/officers in the banking sector. A fuzzy analytic hierarchy process (Fuzzy-AHP) was then used to meet the purpose of the research.

The study identified that the organizational dimension is the most significant criteria for blockchain adoption in the banking sector, followed by the environmental dimension. In contrast, the technological dimension is the least influential criterion. Clientele pressure, IT resources, financial resources, pressure from competitors and relative advantage are the most influential sub-criteria for blockchain adoption.

This study provides valuable insights to bank managers, blockchain and IT developers, third-party service providers and policymakers. For instance, adopting the same blockchain platform is easier for both large and small banks for banking operations by using third-party service provider. At the same time, banks should have the banks' own core team to implement the blockchain-based systems or to have control over the third-party service providers during the adoption stage.

To the best of the authors' knowledge, no empirical studies have used a holistic organizational context to understand the factors influencing the adoption of blockchain technology from traditional to blockchain-based banking systems.

Spiritual tourism is becoming a significant growth area of the Indian travel market, with more Indians opting to go on pilgrimage to popular religious cities. There are many spiritual destinations where some of this life's essences can be sought to enjoy harmony and peace. The study aims to prioritize motivators driving the intentions of the tourists to visit the spiritual destination.

The current study applied the analytical hierarchical process, a multi-criteria decision-making technique, on the sample of visitors from all the six spiritual destinations to rank the motivational factors that drive the intentions of the tourist to visit a spiritual destination.

The study's results postulated that spiritual fulfillment motives and destination atmosphere are the top prioritized motivations, while destination attributes and secular motives emerged as the least prioritized.

The research study provides valuable insights to the spiritual tourism industry stakeholders to target the tourists' highly prioritized motivations to augment the visits to a particular spiritual destination.

Previous research has explored the motivations and modeled their relationships with tourists' satisfaction and intentions. But, the present study has applied a multi-criteria decision-making technique to add value to the existing knowledge base.

In recent years, significant research has focused on the question of whether severe market periods are accompanied by herding behavior. As herding behavior is a considerable cause of the speculative bubble and leads to stock market deviations from their basic values it is necessary to examine the motivators which led to herding behavior among investors. The paper aims to discuss this issue.

In this study, the authors performed a two-phase analysis to address the research questions of the study. In the first phase, for text analysis NVivo software was used to identify the factors driving herding behavior among Indian stock investors. The analysis of a text was performed using word frequency analysis. While in the second phase, the Fuzzy-AHP analysis techniques were employed to examine the relative importance of all the factors determined and assign priorities to the factors extracted.

Results of the study depicted Investor Cognitive Psychology (ICP), Market Information (MI), Stock Characteristics (SC) as the top-ranked factors driving herding behavior, while Socio-Economic Factors (SEF) emerged as the least important factor driving herding behavior.

The current study was undertaken among stock investors from North India only. Moreover, numerous factors are not part of the study but might significantly influence the investors' herding behaviors.

Comprehending the influences of the different factors discussed in the study would enable stock investors to be more aware of their investment choices and not resort to herd behavior. This research enables decision-makers to understand the reasons for herd activity and helps them act accordingly to improve the stock market's performance.

The current study will provide an inclusive overview of herding behavior motivators among Indian stock investors. This study's results can be extremely useful for both academics and policymakers to gain some insight into the functioning of the Indian stock market.

Research in the area of behavioral finance has demonstrated that investors exhibit irrational behavior while making investment decisions. Investor behavior usually deviates from logic and reason, and consequently, investors exhibit various behavioral biases which impact their investment decisions. The purpose of this paper is to rank the behavioral biases influencing the investment decision making of individual equity investors from the state of Punjab, India. This research would provide valuable insight into the different behavioral biases to investors and other participants of the capital market and help them in improving investment decisions.

The research is conducted on the individual equity investors of Punjab, India. Fuzzy analytic hierarchy process was applied to rank the factors influencing the decision making of individual equity investors of Punjab. The primary factors considered for the study are overconfidence bias, representative bias, anchoring bias, availability bias, regret aversion bias, loss aversion bias, mental accounting bias and herding bias.

The three most influential criteria were herding bias, loss aversion bias and overconfidence bias. The five most influential sub-criteria were “I readily sell shares that have increased in value (C61),” “News about the company (Newspapers, TV and magazines) affects my investment decision (C84),” “I invest each element of my investment portfolio separately (C71)” and “I usually hold loosing stock for long time, expecting trend reversal (C52).”

Although sample survey conducted in the present study was based on a limited sample selected from a particular area that truly represented the total population, it is considered as the limitation of this study.

The outcome of this research provides investors with a better understanding of behavioral biases that influence their decision making. This study provides them a guideline on different behavioral biases that they should consider while making investment decisions.

The research model is based on the available literature on behavioral finance and the research results and findings would add value to the existing knowledge base.

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.

Investment decisions hold immense significance for investors and eventually affect their portfolio performance. Investors are advised to weigh the costs and benefits associated with every decision in order to make rational investment decisions. However, behavioral finance research reveals that investors' choices often stem from a blend of economic, psychological and sociological factors, leading to irrationality. Moreover, environmental, social and corporate governance (ESG) factors, aligned with behavioral finance hypotheses, also sway opinions and stock prices. Hence, this study aims to identify how individual equity investors prioritize key determinants of investment decisions in the Indian stock market.

The current research gathered data from 391 individual equity investors through a structured questionnaire. Thereafter, a fuzzy analytic hierarchy process (F-AHP) was used to meet the purpose of the research.

Information availability, representative heuristics belonging to psychological factors and macroeconomic indicators falling under economic factors were discovered to be the three most prioritized criteria, whereas environmental issues within the realm of ESG factors, recommendations of brokers or investment consultants of sociological factors, and social issues belonging to ESG factors were found to be the least prioritized criteria, respectively.

Only active and experienced individual equity investors were surveyed in this study. Furthermore, with a sample size of 391 participants, the study was confined to individual equity investors in one nation, India.

This research has implications for individual investors, institutional investors, market regulators, corporations, financial advisors, portfolio managers, policymakers and society as a whole.

To the best of the authors' knowledge, no real attempt has been made to comprehend how active and experienced individual investors prioritize critical determinants of investment decisions by taking economic, psychological, sociological and ESG factors collectively under consideration.

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.

Small and medium enterprises (SMEs) have been reported as a credit-constrained sector in the earlier literature. Amidst the available external financing options, SMEs are dependent upon banks for their financial needs, hence they offer an important profitable segment for banks. Commercial banks need to develop effective targeting strategies for this segment and ranking the priorities of SMEs in selecting commercial banks will be of great help to them. The purpose of this paper is to implement a fuzzy analytic hierarchy process (FAHP) multi-criteria decision model for commercial bank’s selection by SMEs.

The research process was carried out in two phases. In Phase I, a self-structured scale was developed to measure bank selection criteria of SMEs after an extensive review of the literature of relevant studies on the topic. A sample of 600 SMEs was selected through non-proportionate quota sampling and only 313 valid responses were received. Phase II was conducted to prioritize the extracted factors through FAHP, a multi-criteria decision-making technique. For this purpose, another questionnaire was designed in the form of pair-wise evaluation and the response was taken on the same from those 313 SMEs again.

The results showed that SMEs bank selection criteria can be categorized under six heads, namely, bank attributes, accommodation of credit needs (AC), bank personnel, financial factors (FF), service quality (SQ) and business knowledge. The research study produced a reliable and valid instrument for studying the bank selection criteria of SMEs. The results further revealed that AC is the most important factor considered by SMEs followed by FF and SQ. Going further, global weights were also calculated through the FAHP which revealed that the most important consideration (variable) viewed upon by SMEs is willingness to accommodate credit needs followed by flexible collateral requirements and absence of hidden charges.

The results of the present study offer significant insights as to the factors SMEs consider while making a bank selection decision. It is of utmost importance for banks to identify true determinant factors used by SMEs while making bank choice decisions as they offer ample profit and revenue opportunities to banks. The results of the study provide a practical approach to banks that would help them in framing strategies for SMEs customers.

This is the first study of its kind which has not only focused on the hierarchy of factors measuring bank selection criteria of SMEs rather on the hierarchy of single variables also through the calculation of global weights. As banks cannot focus on all the dimensions of the criteria, they can focus on the spirit of that particular criteria.

In the era of digitalization and technology, tremendous changes have taken place in the taxi industry worldwide. The traditional taxi service has transformed into the latest innovative technology-based e-hailing service. There are innumerable factors that drive the user adoption of e-hailing apps. This study aims to primarily concentrate on identifying, analyzing and ranking these factors which have an impact on the user intention toward using e-hailing apps.

The e-hailing app users in the state of Punjab and Chandigarh are the target population for the study. A fuzzy analytical hierarchy process technique has been applied to analyze and codify the determinants that influence the user intention of adopting e-hailing apps. The primary factors that have been considered for the study are social influence, perceived usefulness, facilitating conditions, perceived ease of use, self-efficacy, perceived risk, compatibility and trust.

The study revealed that “Perceived Usefulness” is the factor that influences user intention to use e-hailing apps the most, while “Perceived Risk” the least. The sub-criteria codified in the top priority was as follows: “Overall, I find the e-hailing app useful in booking a taxi (C15)”; “I do not need some people to use e-hailing apps (C52); “I believe e-hailing app is compatible with existing technology (C61).” The sub-criterion “E-hailing app service provider keeps its promise (C72)” was demonstrated to have the least impact on the user intention of adopting e-hailing apps.

The study has been confined to only eight factors selected from the extended technological acceptance model framework and some related technology acceptance theories. Some more other factors may have an impact on user adoption of e-hailing apps, which need to be added further. Also, the scope of the study should be enhanced by expanding the geographical area beyond the selected region.

The findings of the study enable the e-hailing service providers and marketers to understand the users’ intention in a better way, to make improvements in e-hailing apps and formulate strategies accordingly.

The previous literature provides the base to the present study for identifying the factors affecting user behavioral intention toward e-hailing apps and information technology. The findings and results of the present research make value addition to the existing knowledge base.

Massive open online courses (MOOCs) are among the most recent e-learning initiatives to gain widespread acceptance among universities. However, despite MOOCs' “much-documented” benefits, many questions are being raised late regarding the long-term sustainability of the open online teaching e-learning model. With high dropout rates in MOOCs courses, recent research has focused on the challenges limiting MOOCs’ growth. But most of the research is directed toward students’ perspectives, leaving the instructors’ perspective. One of the most important aspects of instructors’ perspective is the motivation for MOOCs' development and delivery.

The present study collected the data from 25 MOOC developers of Indian origin. To prioritize or rank the motivational factor behind developing a MOOC, a fuzzy-analytical hierarchical process (F-AHP) technique was applied to the data set. The primary motivational factors considered for the study were professional development, altruism, personal development, institutional development, intrigue, monetary benefits and peer influence.

The results showed that professional development and personal development are two prime motives that drive MOOCs development. Monetary benefits and peer influence were the least important factors among all the factors considered for the study.

Previous studies have identified and modeled the motivational factors that contribute toward developing MOOCs. However, there was little knowledge about the hierarchy among the motivating factors. The present study fills this gap by establishing the ranking of motivational factors responsible for MOOCs development.

The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-04-2021-0205.