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This study aims to optimize blood donation drive efficiency by addressing operational bottlenecks and improving resource deployment, focusing on enhancing donor experience and reducing camp duration.

The research uses a mixed-method approach, combining qualitative insights from blood banking officer interviews with quantitative data from 58 camp observations. “Simio” simulation software models various operational configurations, while mathematical techniques like queuing theory analyze key performance metrics. The process involves creating a baseline model, proposing optimizations and validating recommendations through real-world implementation.

The study achieved significant improvements: Reduced average donor time from 1.79 to 0.79 hours (56.4% improvement); Shortened camp operation time from 7.98 to 4.85 hours (39.2% improvement); Decreased waiting times at the medical check station from 45.23 to 0.60 minutes; Improved service rates across all stations, notably at registration (233.79% increase); and Streamlined processes through digitization and health check consolidation.

The study provides actionable recommendations for blood bank managers, including digital pre-registration and optimized staff allocation, leading to substantial time and resource savings while enhancing donor experience.

By improving donation camp efficiency and experience, this research could increase donor retention rates and lead to a more stable blood supply, crucial for medical care.

This research presents a novel approach by combining discrete event simulation with real-world implementation and validation, offering an innovative solution to common bottlenecks in blood donation drives.

Based on the available literature, this paper aims to build a theoretical framework identifying the following five factors influencing electric vehicle (EV) adoption: 1) demographic factors, 2) environmental factors, 3) infrastructural factors, 4) economic factors and 5) fiscal policy factors. The authors then identified the variables representing these factors and built a linear regression model. The authors collected cross-sectional data of the identified variables of Indian states and estimated the regression model using the ordinary least squares method.

India has witnessed a rapid adoption of EVs in the two-wheelers and three-wheelers segment. The paper aims to identify the factors influencing EV adoption in India.

The estimated results indicate that EV adoption is significantly increasing in India due to the rising population and increasing number of power charging stations. The authors also found that government policy incentives have no significant impact on EV adoption.

The study’s findings and the recommended changes in India’s current EV policy aim to help policymakers achieve faster adoption of EVs in India.

The authors further conducted a sentiment analysis of the people’s comments on popular YouTube videos and found that most people have negative sentiments towards EV adoption because of battery-related problems and range anxiety.

Selecting and using the same health monitoring devices for a particular problem is a tedious task. This paper aims to provide a comprehensive review of 40 research papers giving the Smart health monitoring system using Internet of things (IoT) and Deep learning.

Health Monitoring Systems play a significant role in the healthcare sector. The development and testing of health monitoring devices using IoT and deep learning dominate the healthcare sector.

In addition, the detailed conversation and investigation are finished by techniques and development framework. Authors have identified the research gap and presented future research directions in IoT, edge computing and deep learning.

The gathered research articles are examined, and the gaps and issues that the current research papers confront are discussed. In addition, based on various research gaps, this assessment proposes the primary future scope for deep learning and IoT health monitoring model.

As the global financial ecosystem grapples with the complexities of modernization, blockchain technology emerges as a pivotal catalyst, offering the banking, financial services, and insurance (BFSI) industry unprecedented opportunities for secured digital transformation and enhanced customer trust. To gain a comprehensive understanding of blockchain technology adoption, this study aims to identify the factors and establish the contextual interrelationships among them.

The authors have identified the factors affecting blockchain technology adoption in BFSI industry through extensive literature review and experts’ interviews. After identification of factors, contextual relationship has been established based on experts’ opinion and total interpretive structural modeling (TISM) approach. Furthermore, factors are categorized into autonomous, dependent, linkage and driving variables using cross-impact matrix multiplication applied to classification analysis.

The TISM-based structural model is divided into eight different hierarchal levels in which Government support is placed on the lower most layer (level 8) which indicates that this is the most crucial factor in blockchain adoption. Further social influence and security are placed on seventh and sixth level in the hierarchy.

The results of this study will help the policymakers to direct the resources from the most crucial factor to other factors in the hierarchy as per their relevance. In essence, this study serves as a guiding compass, steering the course of blockchain technology adoption in the BFSI sector toward a more secure and digitally transformed future.

In the current landscape, blockchain technology remains in its nascent stage, leaving ample room for exploration and innovation. This study stands as the pioneering effort to comprehensively identify and establish the contextual relationships among the adoption factors of blockchain technology within BFSI industry. Through rigorous TISM analysis, this paper enriches the existing body of knowledge on blockchain technology adoption.

This study aims to explore and conceptualize metaverse adoption using a systematic literature review (SLR). It also aims to propose a conceptual model that identifies significant factors affecting metaverse adoption in the entertainment, education, tourism and health sectors.

A SLR was conducted using the “preferred reporting items for systematic reviews and meta-analyses” report protocol and the “theory, context, characteristics, methods” framework to include all relevant articles published up to March 2023, which were sourced from the Scopus and Web of Science databases.

The reviewed literature revealed that the countries with the highest publications in the field of metaverse were China and the USA. It was also found that the technology acceptance model was the most used theoretical framework. Survey-based research using purposive and convenience sampling techniques emerged as the predominant method for data collection, and partial least square-structural equation modeling was the most used analytical technique. The review also identified the top six journals and the variables that help to develop a proposed model.

This review presents a novel contribution to the literature on metaverse adoption by forming a conceptual model that incorporates the most used variables in the entertainment, education, tourism and health sectors. The possible directions for future research with identified research gaps were also discussed.

While extant literature explores the influence of institutions on the national innovation system (NIS), most research has either focused on specific institutional aspects or treated institutions as a unified entity. This study aims to examine the effect of various institutional factors on a country’s NIS.

The conceptual model was empirically validated using regression analysis. The study sample comprised a total of 84 countries.

This study identifies and empirically validates a comprehensive set of institutional factors. It also highlights the significant institutional factors (including political stability, government effectiveness, ease of resolving insolvency and the rule of law) that can help improve a country’s NIS.

The research provides practical implications for organizations and policymakers seeking to understand and foster an innovative culture within the NIS. Policymakers are encouraged to develop a nurturing environment within the NIS by focusing on significant institutional factors. Organizations are encouraged to closely monitor developments in the NIS of a country to make informed strategic decisions at the business, corporate and international levels.

Understanding consumer behaviour towards ethanol-blended fuel is crucial for assessing the adoption of alternate fuel vehicles (AFVs). By examining factors influencing purchase decisions, such as cost considerations, environmental concerns and perceptions of vehicle performance, researchers can elucidate patterns of consumer acceptance and identify barriers to widespread adoption.

The study uses the theory of planned behaviour (TPB) and stimulus organism response (S-O-R). The current research aims to bridge the gap by focusing on consumers' intention to buy ethanol-blended fuel. Data were collected from 303 survey respondents and analysed using partial least square structural equation modelling (PLS-SEM).

The study finds that adopting motivation, policy incentives, risk perception and sustainable practices significantly influence the intention to purchase ethanol-blended fuel. Surprisingly, cost perception and infrastructure readiness do not have a significant impact on purchase intention.

The study proposes four innovative policy implications to amplify the adoption of ethanol-blended fuel. These policies are (a) flexible fuel vehicle incentive schemes, (b) renewable fuel mandate and certification, (c) ethanol pricing and infrastructure development policy and (d) ethanol urban mobility and public transportation initiatives.

This study provides novel insights into the factors influencing ethanol-blended fuel adoption in India, contributing to the literature on sustainable transportation solutions.

In the present era, artificial intelligence (AI) is transforming and redefining the lifestyles of society through its applications, such as chatbots. Chatbot has shown tremendous growth and has been used in almost every field. The purpose of this study is to identify and prioritize the factors that influence millennial’s technology acceptance of chatbots.

For the present research, data were collected from 432 respondents (millennials) from Punjab. A fuzzy analytical hierarchy process was used to prioritize the factors influencing millennials’ technology acceptance of chatbots. The key factors considered for the study were information, entertainment, media appeal, social presence and perceived privacy risk

The findings of the study revealed media appeal as the top-ranked prioritized factor influencing millennial technology acceptance of chatbots. In contrast, perceived privacy risk appeared as the least important factor. Ranking of the global weights reveals that I3 and I2 are the two most important sub-criteria.

Data were gathered from the millennial population of Punjab, and only a few factors that influence the technology acceptance of chatbots were considered for analysis which has been considered as a limitation of this study.

The findings of this study will provide valuable insights about consumer behaviour to the business firm, and it will help them to make competitive strategies accordingly.

Existing literature has investigated the factors influencing millennials’ technology acceptance of chatbots. At the same time, this study has used the multi-criteria decision-making technique to deliver valuable insights for marketers, practitioners and academicians about the drivers of millennials’ technology acceptance regarding chatbots which will add value to the prevailing knowledge base.

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.

The rapid expansion of artificial intelligence (AI) is progressively reshaping the dynamics of human interaction, communication, lifestyle, education and professional endeavors. The purpose of the study is to comprehend and address the barriers which are impeding the implementation of Generative AI Technologies, such as ChatGPT in the educational landscape.

The study used the Fuzzy analytic hierarchy process (AHP) model to analyze the responses gathered from 149 academicians belonging to the northern states of India.

The study established that the three most important criteria that influence the adoption of generative AI in the education sector are Risk of Academic Integrity, Risk of biased outcomes and Erosion of Critical Thinking.

The present study was confined to Fuzzy AHP to extract the critical criteria influencing the decision-making. Various other techniques such as PF-Delphi and PF-CoCoSo can be used further. The results provide significant inputs for future research to understand the effect of adoption of Generative AI in different contexts including both opportunities and the challenges faced by them.

The study will be beneficial to various stakeholders including students, educators, society and policymakers as the study will highlight the importance of AI tools, introduce the various challenges associated with and explain the use of these tools as productivity-enhancing tools.

To the best of the author’s knowledge, the present study is a novice as the use of AI in Academia is unexplored and the major criteria influencing the choices have yet been undiscovered.