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This study examines the effect of housing environment on residents’ satisfaction and happiness using the data collected from selected residents of Gurugram, an urban locality in India.

Using the convenience-cum-judgmental sampling technique, data was collected from 321 residents of 17 gated private housing estates and tested by performing factor analysis and partial least squares – structural equation modeling.

The results revealed that maintenance service at housing estates significantly influences residents’ satisfaction followed by social infrastructure, dwelling attributes and residential amenities, whereas the accessibility aspect has no significant impact on residents’ satisfaction and happiness. Results also show a significant impact of social infrastructure on residents’ happiness, and most notably, residents’ satisfaction has a significant influence on their happiness.

The findings of this study are likely to provide valuable insight into housing stakeholders (government officials; real estate developers; property and construction professionals, i.e. planners, architects and maintenance managers) to improve the attributes in urban housing setting and neighborhood facilities to upsurge the residents’ satisfaction and happiness level toward the housing estates and townships, which leads to quality and happiness in residents’ life.

According to the authors’ knowledge, the present study is the first to provide an inclusive way toward showcasing the key antecedents of residents’ satisfaction and happiness in the Indian urban housing context. Authors anticipate that future researchers will find present research as a valuable contribution to the residents’ satisfaction and happiness in urban housing planning and revitalization of urban locations.

This study aims to examine the mediating role of green finance in the relationship between green banking practices and the sustainability performance of banking institutions in developing economies.

The authors performed an empirical investigation by applying the “partial least squares structural equation modeling (PLS-SEM)” based on a representative sample of 414 bank employees working in the National Capital Region, India.

The study’s outcome confirms that employee, top-management, operation and policy related practices substantially influence green finance and banks’ sustainability performance. On the contrary, customer related practices insignificantly influence banks’ sustainability performance. Further, green finance substantially influences the sustainability performance of banking institutions.

This study shed light on green banking practices that can assist in achieving the vision of the “Clean India Mission” of the Indian government. In addition, it encourages policymakers and bank managers to fulfill their social responsibility by engaging employees and customers in cleaner operations to promote banks’ sustainability performance.

This is ground-breaking research that enriches the understanding of green banking practices and green finance by providing a novel theoretical framework concerning the sustainability performance of banking institutions. Theoretically, this paper also broadens the scope of corporate social responsibility literature by applying the resource-based view theory in finance and banking.

ChatGPT is an advanced artificial intelligence (AI) form that can generate human-like text based on large amounts of data. This paper aims to empirically examine the ChatGPT adoption level among Indian individuals by considering the key factors in determining individuals’ attitudes and intentions toward newly emerged AI tools.

This paper used “partial least square structural equation modeling” (PLS-SEM) to investigate the relation among several latent factors by applying a representative sample of 351 individuals.

This study found that trialability, performance expectancy and personal innovativeness significantly influence individuals' attitudes, while compatibility and effort expectancy do not significantly impact attitudes. Additionally, trialability, performance expectancy, effort expectancy, personal innovativeness and attitude significantly influence behavioral intentions. However, compatibility has an insignificant impact on behavioral intention. Moreover, the research highlights that attitude and behavioral intention directly correlate with actual use. Specifically, the absence of compatibility makes people hesitate to use technology that does not meet their specific needs.

These unique findings provide valuable insights for technology service providers and government entities. They can use this information to shape their policies, deliver timely and relevant updates and enhance their strategies to boost the adoption of ChatGPT.

This paper is one of the pioneering attempts to exhibit the research stream to understand the individual acceptance of ChatGPT in an emerging country. Moreover, it gained significant attention from individuals for delivering a unique experience and promising solutions.

This study aims to examine the cryptocurrency adoption (CA) level among Indian retail investors who use cryptocurrency as an investment and mode of transaction.

Through self-administered survey questionnaires, data is collected from 397 retail investors of Haryana (India). This study adopted a quantitative method using partial least squares structural equation modeling (PLS-SEM).

This paper offered a robust model with a high explanatory value for CA in which four of the five proposed factors of diffusion of innovation theory (trialability, compatibility, complexity and observability) and one of the two proposed factors of consumer behavioral theory (perceived value) significantly influences CA. More specifically, the absence of regulatory support is a barrier to the broad adoption of cryptocurrencies, as its regulations are necessary to mitigate or minimize uncertain outcomes.

This research primarily focuses on CA in India. Thus, it can be extended to cover diverse other countries for more precise results.

The results provide insights to the government to design the policies, better regulate and make investment strategies that can ultimately enhance CA. In addition, the study’s results also inform financial educators, policymakers, employers and academicians about the significance of several variables affecting CA in India.

From a social standpoint, this study is an advance that directs central banks and governments to develop, regulate and manage digital currencies and implement a digital currency ecosystem. Moreover, the results assist in understanding investors’ perceptions and decision-making perspectives toward cryptocurrencies through the country’s digitalization.

This paper fills the study gap to assist policymakers and cryptocurrency experts in broadening their knowledge base and recognizing prioritized intentions. Additionally, this study provides a theoretical model with the latent variable for a present and pertinent matter.

The present study examines the foremost determinants of teachers' perception, i.e. teachers' satisfaction, attitude and continuance intention towards adopting e-learning in Higher Educational Institutions (HEIs) in India during the COVID-19 pandemic.

Data were collected through online Google forms from 1,111 (1,060 considered useable) teachers of different HEIs in India using the purposive sampling technique and was analyzed by PLS-SEM (performing partial least squares-structural equation modeling).

Results of this study show that perceived usefulness (PU) followed by institutional support, perceived ease of use (PEOU), and teacher-student interaction positively and significantly impact teachers' satisfaction. Results also revealed that perceived usefulness (PU), institutional support, and satisfaction significantly affect teachers' attitude. Finally and most notably, teachers' continuance intention towards using online teaching in HEIs is most significantly influenced by teachers' satisfaction than perceived usefulness (PU), perceived ease of use (PEOU), and attitude.

The authors anticipate that this study brings a significant and valuable input to the existing literature by providing inclusive research in a more harmonizing understanding of the teachers' satisfaction, attitude, and continuance intention with online teaching-learning practices in diverse educational institutions.

The purpose of this paper is to identify the criticality of various sub-systems through the behavioral study of a multi-state repairable system with hot redundancy. The availability of the system is optimized to evaluate the optimum combinations of failure and repair rate parameters for various sub-systems.

The behavioral study of the system is conducted through the stochastic model under probabilistic approach, i.e., Markov process. The first-order differential equations associated with the stochastic model are derived with the use of mnemonic rule assuming that the failure and repair rate parameters of all the sub-systems are constant and exponentially distributed. These differential equations are further solved recursively using the normalizing condition to obtain the long-run availability of the system. A particle swarm optimization (PSO) algorithm for evaluating the optimum availability of the system and supporting computational results are presented.

The maintenance priorities for various sub-systems can easily be set up, as it is clearly identified in the behavioral analysis that the sub-system (A) is the most critical component which highly influences the system availability as compared to other sub-systems. The PSO technique modifies input failure and repair rate parameters for each sub-system and evaluates the optimum availability of the system.

A bottom case manufacturing system is under the evaluation, which is the main component of front shock absorber in two-wheelers. The input failure and repair rate parameters were parameterized from the information provided by the plant personnel. The finding of the paper provides the various availability measures and shows the grate congruence with the system behavior.

The purpose of this paper is to analyze and compare the characteristics of hybrid conventional complementary metal oxide semiconductor/magnetic tunnel junction (CMOS/MTJ) logic gates based on spin transfer torque (STT) and differential spin Hall effect (DSHE) magnetic random access memory (MRAM).

Spintronics technology can be used as an alternative to CMOS technology as it is having comparatively low power dissipation, non-volatility, high density and high endurance. MTJ is the basic spin based device that stores data in form of electron spin instead of charge. Two mechanisms, namely, STT and SHE, are used to switch the magnetization of MTJ.

It is observed that the power consumption in DSHE based logic gates is 95.6% less than the STT based gates. DSHE-based write circuit consumes only 5.28 fJ energy per bit.

This paper describes how the DSHE-MRAM is more effective for implementing logic circuits in comparison to STT-MRAM.

The purpose of this paper is to optimize the performance for complex repairable system of paint manufacturing unit using a new hybrid bacterial foraging and particle swarm optimization (BFO-PSO) evolutionary algorithm. For this, a performance model is developed with an objective to analyze the system availability.

In this paper, a Markov process-based performance model is put forward for system availability estimation. The differential equations associated with the performance model are developed assuming that the failure and repair rate parameters of each sub-system are constant and follow the exponential distribution. The long-run availability expression for the system has been derived using normalizing condition. This mathematical framework is utilized for developing an optimization model in MATLAB 15 and solved through BFO-PSO and basic particle swarm optimization (PSO) evolutionary algorithms coded in the light of applicability. In this analysis, the optimal input parameters are determined for better system performance.

In the present study, the sensitivity analysis for various sub-systems is carried out in a more consistent manner in terms of the effect on system availability. The optimal failure and repair rate parameters are obtained by solving the performance optimization model through the proposed hybrid BFO-PSO algorithm and hence improved system availability. Further, the results obtained through the proposed evolutionary algorithm are compared with the PSO findings in order to verify the solution. It can be clearly observed from the obtained results that the hybrid BFO-PSO algorithm modifies the solution more precisely and consistently.

There is no limitation for implementation of proposed methodology in complex systems, and it can, therefore, be used to analyze the behavior of the other repairable systems in higher sensitivity zone.

The performance model of the paint manufacturing system is formulated by utilizing the available uncertain data of the used manufacturing unit. Using these data information, which affects the performance of the system are parameterized in the input failure and repair rate parameters for each sub-system. Further, these parameters are varied to find the sensitivity of a sub-system for system availability among the various sub-systems in order to predict the repair priorities for different sub-systems. The findings of the present study show their correspondence with the system experience and highlight the various availability measures for the system analyst in maintenance planning.

The aim of this review is to reflect the current state of Financial Technology (FinTech) research along with its journey of development. Further, a conceptual framework showing the interaction of independent, mediating, and moderating variables with dependent variables (acceptance of FinTech products and services) along with propositions is prepared to facilitate the future researchers. This systematic literature review consists of 110 articles from 78 journals indexed in two academic databases (Scopus and/or Web of Science), extracting facts and figures about FinTech during 2016–2021. Our findings contribute to the literature by exemplifying that FinTech is a mixed set of threats and opportunities. In the present review only 18 articles belong to 2016–2017 but 54 articles are considered from 2020–2021, the increasing number of FinTech articles in high-ranking journals indicate the speedily growing popularity of FinTech. Similarly, secondary data based articles are dominating the primary data based ones. Further, regression analysis and PLS-SEM are the most popular statistical techniques among the authors of FinTech articles. To the best of knowledge of the authors, this is a unique study in which the latest FinTech research findings are skimmed.

Phygital emerges as a promising phenomenon, as it uses innovative technologies to connect digital spaces and physical places that provide customers with an interactive and unique experience. Drawing the stimuli–organism–response (S-O-R) framework, the study aims to examine the phygital customer experience by using key drivers and their effect on customer engagement (CE), trust (TRU) and patronage intentions (PI).

Data were obtained by using convenience sampling from 389 respondents from northern parts of India between December 2022 and February 2023. After checking reliability and validity, “variance-based structural equation modeling” has been applied to obtain results.

The outcomes reported that stimuli constructs such as customer brand experience (CBE), service quality (SQ) and emotions during the service (EDS) significantly influence organism (CE). However, pain points (PP) have a statistically insignificant impact on CE. Further, the outcomes also reveal a positive relation between organism and response variables (i.e. CE, TRU and PI).

This study’s results offer strategic insights to enhance CE and PI, ultimately contributing to the advancement of the retail banking industry. The financial service provider must prudently interrelate digital and physical platforms to make the customer journey fruitful.

To the best of the authors’ knowledge, this study is the first to look at the effect of key drivers on the PI of active retail banking customers in national capital region, India by using the S-O-R framework.