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Entrepreneurship, sustainability, and the creation of a resilient, green economy are intricately linked, particularly as conventional economic models grapple with existential challenges. However, empirical research addressing the connection between entrepreneurship and sustainability for a more balanced and resilient future is notably scarce. This chapter aims to bridge this gap by investigating the role of entrepreneurship in advancing sustainability and establishing a resilient, green economy. Through comprehensive research utilizing critical discourse analysis (CDA), three research questions were explored to draw insightful managerial and practical implications. In the intersection of entrepreneurship, sustainability, and a green, resilient economy, opportunities are seized by entrepreneurship amid limitations, while sustainability presupposes responsible management of resources for current needs without compromising the future. A green economy ensures adaptability, growth, and ecological stability even in resource-scarce conditions. The CDA affirms the influential role of entrepreneurship in pursuing sustainability and a green, resilient economy, drawing from 18 cases across public, private, and social sectors to highlight environmental, social, and economic impacts. Furthermore, the CDA uncovers power dynamics, ideologies, and social structures affecting entrepreneurship’s role in fostering sustainable and resilient, green economies. Collaborations between governments, corporations, and social ventures in diverse countries promote sustainability within existing social structures, fostering comprehensive development. However, imbalanced power dynamics pose challenges, risking potential social exclusion. This chapter concludes by addressing practical implications and limitations, aiming to contribute to an ecologically balanced and socially equitable future by understanding entrepreneurship’s role in promoting sustainability and green resilience within the context of power dynamics, ideologies, and social structures.

This study aims to explore the integration of machine learning (ML) in tribology to optimize lubrication interval decisions, aiming to enhance equipment lifespan and operational efficiency through wear image analysis.

Using a data set of scanning electron microscopy images from an internal combustion engine, the authors used AlexNet as the feature extraction algorithm and the J48 decision tree algorithm for feature selection and compared 15 ML classifiers from the lazy-, Bayes and tree-based families.

From the analyzed ML classifiers, instance-based k-nearest neighbor emerged as the optimal algorithm with a 95% classification accuracy against testing data. This surpassed individually trained convolutional neural networks’ (CNNs) and closely approached ensemble deep learning (DL) techniques’ accuracy.

The proposed approach simplifies the process, enhances efficiency and improves interpretability compared to more complex CNNs and ensemble DL techniques.

The aim of the present study was to identify the pattern of intergenerational relations in interdependent society, examine the role of family functioning on it and the role of demographic variables (location, economic class, and familial role) on intergenerational relations, and study the processes of different types of intergenerational relations (solidarity, ambivalence, and conflict). India being an interdependent society emphasizes the family bonds where more than one generation cohabits together. Thus, exploring the intergenerational ties and family dynamics becomes an important factor to examine. The present study anchored in Kagitcibasi’s Family Change Theory investigates intergenerational relationships in India, examining the shift from joint to modified extended families. With 720 participants in urban and rural areas, the research explores cultural attitudes, socioeconomic variables, and coping strategies and intergenerational dynamics within families. Findings reveal prevalent solidarity, ambivalence, and affection, with rural–urban distinctions. The study underscores the importance of understanding intergenerational dynamics, providing insights for scholars, policymakers, and social workers to enhance support networks. The research contributes valuable knowledge to address evolving family needs in the context of changing societal norms and economic factors.

Though academic study on the subject is still in its early stages, there is growing interest in using blockchain technology for transforming the supply chain. The academic literature is divided and yet only includes studies evaluating how the supply chain has changed organizations. To comprehend the new phenomena, this study aims to investigate the factors of blockchain technology in driving supply chain transformation. To be more precise, the authors developed from the literature the most prevalent criteria for determining if supply chain transformations are ready to be scaled up.

This study followed a combination of two multi-criteria decision making methods evaluation based on distance from average solution and complex proportional assessment) methodology in this research: planning, investigating, executing out, establishing a rating of the criteria and evaluating it.

The study shows that the “organizational driver” and the “technology driver” are the factors most important to the transformation of the supply chain, whereas the “financial driver” and the “regulatory driver” are less important. This study also makes some managerial recommendations to address the factors impeding the supply chain’s transformation. Each factor’s significance was explored, and a proposed study agenda was also presented.

Although the main forces behind the transformation of the supply chain have been recognized, further research into statistical correlation is required to confirm how the various elements interact.

This research aids decision-makers in comprehending the key forces behind supply chain transformation. Managers and decision-makers might better predict and allocate the necessary resources to start the road toward digitization and make well-informed choices once these aspects have been investigated and understood.

In light of the pandemic’s effects on the world and the increase in businesses embracing the digital economy, the supply chain transformation is more important than ever. Beyond blockchain deployment and the pilot studies on digital transformation, there is a gap. The topics and factors this study uncovered will operate as a framework and recommendations for more theoretical investigation and practical applications.

Job performance literature has evolved to address the aspects of performance thinking where knowledge sharing (KS) and emotions on the job are considered important. This study evaluates the influence of emotional intelligence (EI), mediated through KS, on job outcomes measured as job performance (JP) and job satisfaction (JS).

Structural equation modeling methods are used to investigate the degree of influence of EI on job outcomes through KS. The data for analysis has been collected from 227 project managers at Information Technology-Information Technology Enabled Services (IT-ITeS) firms.

The findings suggest that the EI has a positive and significant effect on KS. This implies that employees with high EI scores are more involved in the KS behavior. Furthermore, the findings suggest that KS partially mediates the relationship between EI and JP, and it fully mediates the relationship between EI and JS.

For KS, the answer lies in the recruitment and retention of emotionally intelligent persons rather than investing only in information technology. The study enhances the literature on knowledge management, sharing and EI.

The proposed research model suggests a successful infusion of KS within IT-ITeS firms. Knowing the EI of employees and, accordingly, changing their behavior towards sharing knowledge is probably a distinctive, most efficacious and essential strategy for augmenting job outcomes.

The study establishes the role of KS in translating the impact of EI on JP and JS. Furthermore, it contributes a novel framework of KS behavior.

The aim of this work is to establish a computationally efficient iterative technique for solving the highly nonlinear derivative-dependent boundary value problem (DDBVP). The proposed iterative technique is tested on a one-dimensional mathematical model of the shape of the human cornea, which leads to the highly nonlinear DDBVP.

The approach is a combined venture of quasilinearization along with Picard’s iterative technique. The nonlinear DDBVP is simplified to a sequence of linear problems through a quasilinearization technique. Making use of Picard’s iteration approach, an analogous integral form for the quasilinearized DDBVPs is developed in terms of Green’s function and the convergence controller parameter. The study also covered the convergence analysis of the iterative technique.

Numerical illustrations are presented to evaluate the applicability, efficiency and accuracy of the technique. The proposed technique provides approximate numerical solutions to the corneal shape model with a variety of constant parameters that can arise in different physical situations. In comparison with existing works, the proposed coupled iterative approach has been found to be more accurate and less computationally complex.

This study introduces an efficient coupled iterative technique based on quasilinearization and Picard’s iterative technique to solve a one-dimensional model of a human cornea. The proposed approach is tested on various physical situations that affect the human cornea. Numerical findings are compared with other published results to demonstrate the method’s efficiency and ease of usage.

This research paper explores customer experience (CX) among low-literate customers in organized retail environments. It integrates theories from customer literacy, CX and patronage literature to understand CX comprehensively.

The study gathered data from 470 respondents using mall intercept and snowball sampling. Data analysis employed partial least squares (PLS) modeling.

The results indicate that all the dimensions do not have the same effect on CX. Answering calls for future research, the results establish CX's nomological validity by showing its positive influence on retail reputation, retail quality and satisfaction. However, it does not directly affect patronage but has an indirect influence through retail quality and satisfaction. Also, the authors conclude that retail quality and satisfaction are consequences of CX and not previously conceptualized proxies for it.

Conducting primary research with low-literate customers (LLCs) has its own set of limitations that give rise to further research directions. While acknowledging limitations, the study suggests avenues for future research by surveying LLCs with an objective questionnaire, contributing to limited empirical research in this segment.

The findings highlight the multidimensional nature of CX. In summary, this research paper provides insights into CX dimensions and outcomes for LLCs in organized retail. It contributes to marketing literature, assisting retailers in improving CX and driving patronage across customer segments.

The paper contributes to marketing literature by studying LLCs, testing a comprehensive CX model, confirming antecedents in retail patronage and exploring reciprocal relationships in retailing.

This study aims to focus on carrying out a quantitative analysis of publications related to augmented reality (AR)-based gaming. AR combines the real world with the digital world and is a large basis of data in an ICT-based digital society. Therefore, there is a need for a comprehensive bibliometric review of research papers on AR-based gaming to identify the potential for the advancement of knowledge in this field.

In the experimental part, reference data from Scopus was extracted according to a search string formulated with great care. The retrieved data were then analyzed through open-source, sophisticated bibliometric tools, VOSviewer, along with Biblioshiny-R.

After running the analysis, it turns out that the years with the biggest number of publications were in 2010 and 2012. The focus areas of study include mixed reality, surveys and helmet-mounted displays. The USA was the most connected country in the research collaboration network; it has strong links with other nations including the UK, Canada and Greece, which cluster around it in the map. This trend of monetarily increasing number of papers depicts the growing interest and sustainable productivity in this research domain.

The purpose of this paper is to present a novel perspective on AR-based gaming, which has not been so far examined by bibliometric analysis. The results provide important information for researchers and stakeholders. In addition to filling a void in the literature, this study is an important reference for those researchers who are interested in exploring evolution and trends in AR-based gaming research.

This chapter investigates pandemic impact in a variety of industries, including food, travel, education and pharmaceuticals, considering elements such as isolation, emotions and social influences, which can lead to panic buying. The goal of this research is to ascertain how COVID-19 influences the buying decisions of customers. Additionally, the study aims to identify consumer consumption trends for a spectrum of products and services, including fast-moving consumer goods (FMCGs), entertainment, pharmaceuticals, travel and tourism. A comprehensive review of different research papers is done to conclude. The papers considered are from 2020 to 2022. Different keywords are used to search the relevant papers such as ‘pandemic’, ‘COVID-19’, ‘behaviour’, ‘impulsive’, etc. TCCM framework has been applied while reviewing the articles. During the isolation, consumer behaviour moved to panic buying and stockpiling, favouring organic basics, and encouraging e-commerce, as well as economic nationalism favouring made-in-India products. This study helps in knowing the reasons for change in consumers' behaviour for different products and services due to unforeseeable situations like COVID-19 and can find possible ways to deal with them. Business owners learn about changing consumer purchasing behaviours and how to modify products. The government can change policies to improve medical tourism and social protection.

Artificial Intelligence (AI) is an emerging technology and turned into a field of knowledge that has been consistently displacing technologies for a change in human life. It is applied in all spheres of life as reflected in the review of the literature section here. As applicable in the field of libraries too, this study scientifically mapped the papers on AAIL and analyze its growth, collaboration network, trending topics, or research hot spots to highlight the challenges and opportunities in adopting AI-based advancements in library systems and processes.

The study was developed with a bibliometric approach, considering a decade, 2012 to 2021 for data extraction from a premier database, Scopus. The steps followed are (1) identification, selection of keywords, and forming the search strategy with the approval of a panel of computer scientists and librarians and (2) design and development of a perfect algorithm to verify these selected keywords in title-abstract-keywords of Scopus (3) Performing data processing in some state-of-the-art bibliometric visualization tools, Biblioshiny R and VOSviewer (4) discussing the findings for practical implications of the study and limitations.

As evident from several papers, not much research has been conducted on AI applications in libraries in comparison to topics like AI applications in cancer, health, medicine, education, and agriculture. As per the Price law, the growth pattern is exponential. The total number of papers relevant to the subject is 1462 (single and multi-authored) contributed by 5400 authors with 0.271 documents per author and around 4 authors per document. Papers occurred mostly in open-access journals. The productive journal is the Journal of Chemical Information and Modelling (NP = 63) while the highly consistent and impactful is the Journal of Machine Learning Research (z-index=63.58 and CPP = 56.17). In the case of authors, J Chen (z-index=28.86 and CPP = 43.75) is the most consistent and impactful author. At the country level, the USA has recorded the highest number of papers positioned at the center of the co-authorship network but at the institutional level, China takes the 1st position. The trending topics of research are machine learning, large dataset, deep learning, high-level languages, etc. The present information system has a high potential to improve if integrated with AI technologies.

The number of scientific papers has increased over time. The evolution of themes like machine learning implicates AI as a broad field of knowledge that converges with other disciplines. The themes like large datasets imply that AI may be applied to analyze and interpret these data and support decision-making in public sector enterprises. Theme named high-level language emerged as a research hotspot which indicated that extensive research has been going on in this area to improve computer systems for facilitating the processing of data with high momentum. These implications are of high strategic worth for policymakers, library stakeholders, researchers and the government as a whole for decision-making.

The analysis of collaboration, prolific authors/journals using consistency factor and CPP, testing the relationship between consistency (z-index) and impact (h-index), using state-of-the-art network visualization and cluster analysis techniques make this study novel and differentiates it from the traditional bibliometric analysis. To the best of the author's knowledge, this work is the first attempt to comprehend the research streams and provide a holistic view of research on the application of AI in libraries. The insights obtained from this analysis are instrumental for both academics and practitioners.