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This study explores the factors impacting user adoption and trust in blockchain-based food delivery systems, with a spotlight on the Open Network for Digital Commerce (ONDC). In the evolving food delivery sector, blockchain offers transparency and efficiency. Through the Unified Theory of Acceptance and Use of Technology (UTAUT) lens, this research provides insights for businesses and policymakers, highlighting the importance of blockchain’s integration into food delivery.

The research employed the UTAUT and its extensions as the theoretical framework. A structured questionnaire was developed and disseminated to users of the ONDC platform, and responses were collected on a seven-point extended Likert scale. The analyses were undertaken employing the partial least squares (PLS) methodology and structural equation modelling (SEM).

Key factors like performance expectancy, effort expectancy and social influence were found influential for adoption. Trust played a central role, while perceived risk didn’t significantly mediate the adoption process. Digital culture didn’t significantly moderate the adoption intention.

This research adds to the existing body of knowledge by providing empirical insights into user adoption and trust in blockchain-based food delivery platforms. It is among the pioneer studies to apply the UTAUT model in the realm of blockchain-based food delivery platforms, thereby offering a unique perspective on the dynamics of user behaviour in this emerging field.

This paper aims to explore the capabilities of virtual reality (VR) and augmented reality (AR) in enhancing the tourism experience and addressing physical travel constraints.

A survey was conducted among 412 tourists who had used VR and AR technologies for tourism purposes. Data was analysed using descriptive statistics, multidimensional scaling, multiple regression analysis, structural equation modelling and mediation and moderation analysis.

VR and AR enhance tourism via immersive experiences. They overcome traditional travel barriers, but accessibility concerns, like cost, persist. The perceived potential of these technologies strongly predicts future adoption, emphasising the need to highlight their immersive benefits.

This study offers ground-breaking insights into VR and AR in tourism. It acknowledges inherent limitations in accessibility, with VR and AR technologies not being universally adoptable due to costs and complexity. Theoretically, the research enriches academic discourse by highlighting the pivotal role of perceived potential in technology adoption. It also delineates the intricate relationship between VR/AR experience, satisfaction, destination image and revisit intention.

The findings emphasise crafting immersive VR and AR experiences for differentiation. By integrating these technologies into strategic marketing, organisations can enhance customer satisfaction and provide richer destination experiences.

The research underscores VR and AR’s potential to foster inclusive tourism, benefitting those with limitations. Additionally, promoting cultural exchange and sustainability, these technologies can drive societal harmony and contribute to economic growth through enriched tourism experiences.

This study stands out in its distinctive exploration of VR and AR in the tourism domain. The innovation lies in its comprehensive examination of the technologies’ immersive capabilities and their accessibility challenges. The value is accentuated by its insights, which bridge the gap between technological potential and its practical application in tourism.

We aim to analyze the capital structure heterogeneity for manufacturing and service sector firms. Additionally, we analyze the impact of the COVID-19 pandemic on the leverage adjustments of corporate firms.

This study applies the two-step system generalized method of moments (system-GMM) and panel data of 1,115 manufacturing and 482 service sector firms listed with the Bombay Stock Exchange (S&P BSE) from 2010 to 2023. We developed and analyzed three models. Model 1 analyzes the leverage determinants and speed of adjustment (SOA) for the manufacturing and service sectors. Model 2 evaluates the leverage SOA for various sub-sectors, and Model 3 analyzes the impact of the COVID-19 pandemic on the leverage SOA.

This study suggests the three following. First, the direction of leverage determinants suggests that manufacturing firms are highly tangible. In contrast, service sector firms are high-growth firms and recorded a higher SOA (12.01%) than manufacturing (9.09%). Second, analyzing the leverage heterogeneity, we found that SOA varies across the sub-sectors. For manufacturing, food and beverage sub-sector recorded the highest SOA (12.58%), while consumer durables reported the lowest (6.38%). Communication recorded the highest (24.15%) for services, while industrial services recorded the lowest (11.18%). Third, firms across sectors and sub-sectors increased their SOA during COVID-19 pandemic.

This in-depth analysis of leverage heterogeneity for different sectors and subsectors will assist policymakers, corporate managers and other stakeholders in making agile financial decisions.

The analysis of leverage heterogeneity for the manufacturing and service sector from the emerging Indian economy marks a novel contribution to existing literature.

Supply chain network design is an important strategic decision that firms make considering both the short‐ and long‐term consequences of the network's performance. The typical design approach implicitly assumes that, once designed, the facilities and the links will always operate as planned. In reality, however, facilities and the links connecting them, fail from time to time due to poor weather, natural or man‐made disasters, or a combination of any other factors. This work aims to propose a design framework that addresses the facility and link failures explicitly by accounting for their impact on a network's performance measures of efficiency and robustness.

The study incorporated a robustness metric for evaluating the resiliency of supply chains in the case of a network disruption. This robustness metric is based on expected losses incurred due to network failures. It defines efficiency and robustness in terms of operational cost and expected disruption cost (EDC), respectively. The EDC is defined in terms of loss of opportunity cost incurred due to not meeting demand on time after a disruption has occurred. The study used a scenario planning approach and formulated a mixed integer linear program model with the objective of maximizing both efficiency and robustness. It also evaluates the trade‐offs between efficiency and robustness.

The resulting supply chain is much more reliable in the long term since we have shown that a significant amount of robustness can be built into the system without compromising a lot on efficiency.

This work demonstrates a methodology which incorporates such disaster scenarios into the design of a supply chain network. This leads to a more reliable supply chain which would lead to higher profitability and lower disruption rates.

The purpose of this paper is to describe a new approach to sentence representation learning leading to text classification using Bidirectional Encoder Representations from Transformers (BERT) embeddings. This work proposes a novel BERT-convolutional neural network (CNN)-based model for sentence representation learning and text classification. The proposed model can be used by industries that work in the area of classification of similarity scores between the texts and sentiments and opinion analysis.

The approach developed is based on the use of the BERT model to provide distinct features from its transformer encoder layers to the CNNs to achieve multi-layer feature fusion. To achieve multi-layer feature fusion, the distinct feature vectors of the last three layers of the BERT are passed to three separate CNN layers to generate a rich feature representation that can be used for extracting the keywords in the sentences. For sentence representation learning and text classification, the proposed model is trained and tested on the Stanford Sentiment Treebank-2 (SST-2) data set for sentiment analysis and the Quora Question Pair (QQP) data set for sentence classification. To obtain benchmark results, a selective training approach has been applied with the proposed model.

On the SST-2 data set, the proposed model achieved an accuracy of 92.90%, whereas, on the QQP data set, it achieved an accuracy of 91.51%. For other evaluation metrics such as precision, recall and F1 Score, the results obtained are overwhelming. The results with the proposed model are 1.17%–1.2% better as compared to the original BERT model on the SST-2 and QQP data sets.

The novelty of the proposed model lies in the multi-layer feature fusion between the last three layers of the BERT model with CNN layers and the selective training approach based on gated pruning to achieve benchmark results.

The purpose of this study is to explore the machining characteristics of electrical discharge machining (EDM) when a tool is fabricated using powder metallurgy. Because pure Cu tools obtained using conventional machining pose problems of high tool wear rate, tool oxidation causes loss of characteristics in tool shape.

The research investigation carried out experiments planned through Taguchi’s robust design of experiments and used analysis of variance (ANOVA) to carry out statistical analysis.

It has been found that copper and chromium electrodes give less metal removal rate as compared to the pure Cu tool. Analytical outcomes of ANOVA demonstrated that MRR is notably affected by the variable’s polarity, peak current, pulse on time and electrode type in the machining of EN9 steel with EDM, whereas the variables pulse on time, gap voltage and electrode type have a significant influence on EWR. Furthermore, the process also showed that the use of powder metallurgy tool effectively reduces the value of SR of the machined surface as well as the tool wear rate. The investigation exhibited the possibility of the use of powder metallurgy electrodes to upgrade the machining efficiency of EDM process.

There is no major limitation or implication of this study. However, the composition of the powders used in powder metallurgy for the fabrication of tools needs to be precisely controlled with careful control of process variables during subsequent fabrication of electrodes.

To the best of the authors’ knowledge, this is the first study that investigates the effectiveness of copper and chromium electrodes/tools fabricated by means of powder metallurgy in EDM of EN9 steel. The effectiveness of the tool is assessed in terms of productivity, as well as accuracy measures of MRR and surface roughness of the components in EDM machining.

Building on the social cognitive theory, a mediation model was examined to understand the role of teacher self-efficacy as the underlying mechanism for the relationship between instructional leadership and teacher job satisfaction.

The study tests a mediation model between instructional leadership, teacher self-efficacy and job satisfaction. The data were collected via online survey from primary school teachers (N = 320) working for the Municipal Corporation of Delhi (MDC) in India. The mediation model was tested using the AMOS 22.0 after establishing the reliability and validity of measures.

Regression analyses using the bootstrapping method indicated that teacher self-efficacy mediates the relationship between instructional leadership and teacher job satisfaction.

This is a cross-sectional study. The scope for causal inferences is, thus, limited.

In the Indian setting, the study examines the association between instructional leadership and job satisfaction. The results show that the instructional leadership of the school principal is strongly related to teachers' self-efficacy, which, in turn is positively associated with teacher’s job satisfaction. Further, the findings confirm that instructional leadership, emphasizing instructional improvement, improves teachers' self-efficacy and job satisfaction.

The study explains the underlying process through which a school principal’s instructional leadership is related to teacher job satisfaction. This study is perhaps the first to focus on an Indian or a non-Western context.

This paper aims to assess the factors favoring the adoption of the challenges faced and support mechanism, which will lead to the proliferation of glass fiber-reinforced gypsum (GFRG) technology in India.

Semi-structured interviews with 35 experts, including construction developers, architects, contractors, government officials and design consultants, were conducted. This qualitative data was analyzed using thematic analysis and matrix analysis.

GFRG-based buildings produce much less carbon footprints as compared to traditional ones and can be safely recommended as a promising, environmentally sensitive technology of the future. The major drivers in its adoption are its efficient construction capability, energy and soil conservation and significant waste reduction. Some of the challenges in implementation are long planning time, lack of skilled labor, lack of awareness about green building technologies and myopic perception of high cost incurred in green building adoption in people’s minds.

This study establishes that the construction industry has the potential to contribute toward creating a sustainable and green planet. It does so by evaluating and then positively positioning GFRG as an environmentally friendly building system.

The harmful effects of continuous environmental manipulation by humans leading to its degradation is a critical discussion agenda for most nations of the world. The issue has been taken up seriously by developing countries, and now, developing countries are also becoming sensitised to it. Several policies toward the attainment of this goal have been formulated and are being implemented by government and private bodies. Although some authors have studied the issues and challenges related to the adoption of green buildings, their attempts mostly focused on developed countries. Moreover, research that investigated the evaluation of the GFRG building system as a successful green technology of the future is inadequate.

Purpose: This chapter discusses the role and use of chatbots adopted by the different categories of banks (private and public sector banks) in India. The chapter presents brief essential services offered by Indian chatbots regarding accuracy, technology providers and virtual assistance, ways to connect, etc. This chapter concluded that most of the questions answered by the Indian chatbots are already available on the banks’ websites, and there is a need for enhancement in the capabilities of Indian chatbots.

Need for the Study: The need for the study is based on the working of banking chatbots, customer query handling, and the efficiency of the chatbots in India. The chapter helps to analyze the services offered by various banks.

Methodology: This chapter is based on secondary data collected from banks’ websites and articles from various journals. The study is based on nine banks (both private and public sectors) those are having working chatbots (SBI, HDFC Bank, ICICI Bank, Yes Bank, IndusInd Bank, Kotak Mahindra Bank, Axis Bank, Andhra Bank, Bank of Baroda). The present study is focused on chatbots, their services, and software applications for various customer-handling capacities.

Findings: The research concluded that Indian banks are investing a small amount in using chatbots, yet Indian chatbots are deficient regarding far too provincial administrations as they are adequate just for standard and basic inquiries. Also, Indian customers are not properly aware of chatbots and virtual assistance.

Practical Implications: This study provides an overview of the working chatbots in India (for both public and private sector banks) and their functions, as well as the capacities of these chatbots. The previous conducted studies are based on the uses, importance, and working of chatbots/artificial intelligence (AI) in banking. In this study, after discussing the different services, it is found that Indian banks need to update their AI/Virtual assistance with more features.

This chapter is focused on India's destination marketing strategies that promote religious tourism. It sheds light on the Government of India's initiatives to attract faithful tourists to sacred locations including holy temples and places of interest for spiritual pilgrims. The tourism business in India has gone through numerous phases of growth. This research reveals how the state government and central governments have stepped up their commitment to develop tourism, including religious tourism, on multiple fronts. It confirms that India can be rightly considered as the land of faith, as spirituality and religion are very prominent, as evidenced by its holy temples and landmarks, located in different regions of the subcontinent. In conclusion, it discusses about the challenges for the future, and elaborates on the opportunities related to promoting religious tourism to target faithful pilgrims and other visitors to “Incredible India.”