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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.

The emergence of virtual reality (VR) has the potential to revolutionize various industries, including tourism, as it delivers a simulated environment that closely emulates real-life experiences. Therefore, this study aims to explore how the factors, i.e. enjoyment, emotional involvement, flow state, perceived privacy risk, physical risk and cost, influence the customers’ intention to use VR for tourism.

This study integrates the technology acceptance model, hedonic consumption theory with other factors, including cognitive response, authenticity, perceived privacy risk, perceived physical risk, perceived cost and perceived presence. Partial least squares structural equation modelling approach was used to test the proposed research model.

The finding based on the sample of 252 respondents revealed that authenticity is the most influential factor impacting behavior intention followed by perceived cost, attitude, cognitive response and enjoyment. Also, the study supported the moderating impact of personal innovativeness between attitude and behavioral intention to use VR for tourism.

The findings of the study offers practical implications for service providers, site managers, destination marketers, tourist organizations and policymaker to develop more effective strategies for offering VR services for tourism.

This study enriches the current understanding of VR adoption in context of tourism with empirical evidences.

虚拟现实的出现有可能彻底改变包括旅游在内的多个行业, 因为它提供了一个模拟环境, 密切模拟真实生活体验。因此, 本研究旨在探讨愉悦、情感投入、流体状态、感知隐私风险、身体风险和成本等因素如何影响顾客使用虚拟现实进行旅游的意愿。

本研究将TAM模型、享乐消费理论与其他因素相结合, 包括认知反应、真实性、感知隐私风险、感知身体风险、感知成本和感知存在。 PLS SEM 方法用于测试所提出的研究模型。

基于 252 名受访者样本的研究结果表明, 真实性是影响行为意图的最大因素, 其次是感知成本、态度、认知反应和享受。此外, 该研究还支持个人创新性对使用虚拟现实进行旅游的态度和行为意图之间的调节影响。

该研究的结果为服务提供商、站点管理者、目的地营销人员、旅游组织和政策制定者制定更有效的策略来为旅游业提供 VR 服务提供了实际意义。

本研究通过实证证据丰富了当前对旅游背景下虚拟现实采用的理解。

La aparición de la realidad virtual tiene el potencial de revolucionar diversas industrias, incluyendo el turismo, ya que proporciona un entorno simulado que emula de cerca las experiencias de la vida real. Por lo tanto, este estudio tiene como objetivo explorar cómo los factores, como la diversión, la participación emocional, el estado de flujo, el riesgo percibido de privacidad, el riesgo físico y el costo, influyen en la intención de los clientes de utilizar la realidad virtual para el turismo.

Este estudiointegra el modelo TAM, la teoría del consumohedónico con otrosfactores que incluyen la respuestacognitiva, la autenticidad, el riesgo de privacidadpercibido, el riesgofísicopercibido, el costopercibido y la presenciapercibida. Se utilizó el enfoque PLS SEM para probar el modelo de investigaciónpropuesto.

El hallazgobasadoen la muestra de 252 encuestadosreveló que la autenticidades el factor másinfluyente que afecta la intención de comportamiento, seguido del costopercibido, la actitud, la respuestacognitiva y el disfrute. Además, el estudioapoyó el impactomoderador de la innovación personal entre la actitud y la intención de comportamiento al utilizar la realidad virtual para el turismo.

Los hallazgos del estudioofrecenimplicacionesprácticas para que losproveedores de servicios, administradores de sitios, comercializadores de destinos, organizacionesturísticas y formuladores de políticasdesarrollenestrategiasmásefectivas para ofrecerservicios de realidad virtual para el turismo.

Este estudioenriquece la comprensión actual de la adopción de la realidad virtual en el contexto del turismo con evidenciasempíricas.

Purpose: There has been a growing debate on whether generative AI can serve as the modern-day equivalent of white-collar knowledge workers. In a recent post, technology magnate Bill Gates boldly proclaimed that ChatGPT would soon become the quintessential white-collar worker of tomorrow (Dean, 2023). This is indeed an exciting prospect, as generative AI advances at breakneck speed.

Need for the Study: This research delves upon the implications of such advancements for industries reliant on skilled employees. It raises questions about how these individuals will adjust their skillset going forward, given the proliferation of generative AI solutions poised to disrupt traditional roles previously occupied by humans.

Methodology: The study uses an exploratory framework to understand AI’s implications on job roles, productivity, and skill requirements. It introduces generative AI and its relevance, focusing on how it could transform white-collar jobs.

Findings: One thing seems clear: its impact on future employment opportunities. However, this technology still has limitations, potentially leading to unintended consequences. While capable of performing certain functions precisely and accurately, it cannot fully replace the reasoning abilities or cognitive flexibility innate in human workers tasked with knowledge-based work.

Practical Implications: The potential implications for workforce development, policy-making, and future research in AI and labor economics are highlighted. This will also help gain insights into the integration process, benefits, challenges, and the changing nature of white-collar work due to generative AI.

This research paper explores the adoption and impact of Human Resource (HR) Analytics and Artificial Intelligence (AI) in the Information Technology (IT) sector. The study involves interviews with HR experts in the IT industry to understand their perceptions and experiences with these technologies.

Data were collected through semi-structured interviews, in which fifteen HR managers were interviewed.

The findings reveal that HR Analytics and AI significantly impact HR functions, capabilities, and decision-making in the IT sector. To successfully adopt HR analytics and AI, HR professionals must possess technical skills such as data analysis, coding, analytical thinking, design thinking, and domain knowledge. The interviewees also highlighted the importance of connecting HR initiatives with financial outcomes, creating strategies, contributing to decision-making processes, and aligning activities with organizational objectives.

This paper incorporates insights on challenges in adopting analytics and AI in the HR domain along with strategies to overcome.

The aim of the current study is to inspect the influence of high temperatures on the compressive and split-tensile-strength (STS) of concrete mixtures produced by replacing natural river sand with waste-foundry sand (WFS) at 25%, 50%, 75% and 100%. When the experimental findings and the projected outcomes were compared by IS:456-2000 code equations, the STS results predicted by the suggested mathematical equations exhibit lower variations. It is proposed to employ the presented mathematical formulas to evaluate the STS of concrete cylindrical specimens at higher temperatures.

After fabricating, concrete mixtures were allowed to cure for 28 days. For the purpose of avoiding explosive spalling during the heating process, concrete samples are taken out from the curing chamber after 28 days and allowed to dry for two days. The manufactured concrete specimen is exposed to 100 °C, 200 °C, 300 °C, 400 °C, 500 °C and 600 °C temperatures for a duration of 2 h. After the specimens have cool down to room temperature (RT), the physical test, ultrasonic-pulse-velocity (UPV) test, compressive strength test and STS test are carried out.

With an increase in WFS content, concrete specimens' residue compressive-strength and STS decreases. The STS of samples declines as the WFS content rises with increase in temperature interval. According to the UPV test, the concrete samples quality is “good” up to 400 °C; after 500 °C, it ranges from “doubtful to poor.” The UPV values of various mixes declined as the temperature increased. Mass losses increase with exposure to greater temperatures and with an increase in the proportions of WFS in concrete specimens. For mixtures MWFS-0, MWFS-1, MWFS-2, MWFS-3 and MWFS-4 (0%, 25%, 50%, 75% and 100% WFS content), no cracks were present on any of the samples below 400 °C. Concrete surfaces start to show cracks whenever the intervals of temperature increase above 400 °C.

In this investigation, WFS elements are totally substituted for natural sand in concrete mixtures. The residue strength properties, including residual compressive strength and residual STS, were found to be lower after exposures to greater temperature when comparisons were made to referral mixtures. When comparing specimens’ compressive strength, higher temperatures have more effects on the STS of samples with higher WFS contents.

Purpose: The surge in utilising big data, machine learning (ML), and artificial intelligence (AI) has significantly strengthened financial systems, enhancing their robustness, safety, and fairness. AI in finance is profoundly reshaping our lives. This study investigates AI’s role in personal and corporate finance.

Methodology: A comprehensive literature review was conducted to examine studies on the application of AI in personal and corporate finance.

Findings: The financial services sector is undergoing an AI-led transformation, leveraging technologies such as deep learning, collaborative filtering, support vector machines, automation, robotic assistance, and AI-based predictive modelling. In personal finance, AI delivers personalised insights, automates procedures, detects fraud, and enhances financial literacy. It empowers individuals to manage their finances more effectively by tracking expenditures, aiding in budget management, and setting and monitoring financial goals. In corporate finance, AI aids in detecting and analysing credit risks, improving loan underwriting, and minimising financial risk. It also helps reduce financial crimes and enhances the financial performance of accounting firms. AI’s role in bookkeeping, accounting, and financial management significantly benefits entrepreneurs, business executives, investors, and bankers involved in starting and growing organisations.

Practical Implications: In personal finance, AI enhances financial planning, automates transactions, improves credit scoring, and strengthens fraud detection. In corporate finance, AI facilitates advanced risk management, precise financial forecasting, cost reduction through automation, and optimised investment strategies.

Introduction: Regulatory technology (Regtech) uses technology and innovative techniques to improve and expedite regulatory compliance procedures across various sectors, notably the financial industry. This chapter closely examines how Regtech is being used in the financial sector.

Purpose: This study examines the adoption and implementation of Regtech solutions in the financial sector. It aims to identify the leading technologies propelling the adoption of Regtech and the benefits and challenges financial institutions face when implementing Regtech solutions for regulatory compliance.

Need of the Study: The study examines the increasing demand for effective regulatory compliance in the financial sector. Financial institutions seek novel ways to streamline compliance procedures as regulatory complexities increase. This study fulfills the need to examine the adoption of Regtech as a potential solution to enhance regulatory compliance’s efficiency and effectiveness.

Research Methodology: This chapter’s research methodology entails a comprehensive review of existing studies, and examples to collect pertinent data and insights. The current study provides an extensive and comprehensive view of the existing studies. Examining real-world examples provides practical examples and demonstrates the efficacy of Regtech in the financial sector.

Findings: The findings reveal that the adoption of Regtech is gaining momentum in the financial industry, with increasing recognition of its potential benefits. Financial institutions leverage Regtech solutions to automate compliance processes, monitor regulatory changes, and enhance risk management practices. However, technological complexities, regulatory constraints, and data security concerns pose significant hurdles to widespread adoption.

This study aims to characterize the behavior of blended concrete, including metakaolin (MK) and quarry dust (QD), as supplementary cementing materials. The study focuses on evaluating the effects of these materials on the fresh and hardened properties of concrete.

MK, a pozzolanic material, and QD, a fine aggregate by-product, are potentially sustainable alternatives for enhancing concrete performance and reducing environmental impact. The addition of different percentages of MK enhances the pozzolanic reaction, resulting in improved strength development. Furthermore, the optimum dosage of MK, mixed with QD, and mechanical properties like compressive, flexural and split tensile strength of concrete were evaluated to investigate the synergetic effect of MK and quarry dust for M20-grade concrete.

The results reveal the influence of metakaolin and QD on the overall performance of blended concrete. Cost analysis showed that the optimum mix can reduce the 7%–8% overall cost of the materials for M20-grade concrete. Energy analysis showed that the optimum mix can reduce 7%–8% energy consumption.

The effective utilization is determined with the help of the analytical hierarchy process method to find an optimal solution among the selected criteria. According to the AHP analysis, the optimum content of MK and quarry dust is 12% and 16%, respectively, performing best among all other trial mixes.

Research in the domain of behavioral finance has proven that investors demonstrate irrational behavior while making investment decisions. In a similar domain, the primary objective of this research is to prioritize the behavioral biases that influence cryptocurrency investors' investment decisions in the Indian context.

A fuzzy analytic hierarchy process (F-AHP) was used to prioritize the behavioral factors impacting cryptocurrency investors' investment decisions. Overconfidence and optimism, anchoring, representativeness, information availability, herding, regret aversion, and loss aversion are among the primary biases evaluated in the present study.

The findings suggested that the two most important influential criteria were herding and regret aversion, with loss aversion and information availability being the least influential criteria. Opinions of family, friends, and colleagues about investment in cryptocurrency, the sale of cryptocurrencies that have increased in value, the avoidance of selling currencies that have decreased in value, the agony of holding losing cryptocurrencies for too long rather than selling winning cryptocurrencies too soon, and the purchase of cryptocurrencies that have fallen significantly from their all-time high are the most important sub-criteria.

This survey only covered active cryptocurrency participants. Additionally, the study was limited to individual crypto investors in one country, India, with a sample size of 467 participants. Although the sample size is appropriate, a larger sample size might reflect the more realistic scenario of the Indian crypto market.

The study is relevant to individual and institutional cryptocurrency investors, crypto portfolio managers, policymakers, researchers, market regulators, and society at large.

To the best of the authors' knowledge, no prior research has attempted to explain how the overall importance of various criteria and sub-criteria related to behavioral factors that influence the decision-making process of crypto retail investors can be assessed and how the priority of focus can be established, particularly in the Indian context.