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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.

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 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.

The COVID-19 pandemic, having emerged early in the year 2020, had a devastating impact on the whole aviation industry. Airlines were particularly hit hard, given unprecedented border closures, the inability to adapt to highly volatile demand, and an embarrassing lack of overall pandemic preparedness. As of summer 2022, the airline industry seems on the track of strong recovery, with many airlines returning to profits and passenger numbers occasionally exceeding those of pre-COVID-19 levels. This study investigates the induced pandemic cycle, from January 2020 to December 2022, through tools from the network science literature. We model airline networks as a collection of nodes, representing airports, and a collection of links, representing direct flights between airports. This analysis has a strong focus on spatiotemporal evolution of airlines worldwide during the COVID-19 pandemic, leading to a comprehensive description of network science effects and counter measures during this excessive health and economic shock. Such an analysis could be helpful for better dealing with future pandemics, which are likely to emerge, if potential lessons learned are not implemented by the aviation industry.

In this article, the authors try to determine why and under what conditions consumers intend to buy green and what the consequences are. Relying on theories of reasoned action and theory of planned behaviour (TPB), the authors offer that the green purchase intention (GPI) is impressed by environmental and personality components. Provide statements about the determinants and key implications of such market identification.

This paper aims to conceptualize and define a construct of brand pride and develop a measurement instrument for the construct.

A scale development procedure comprising a qualitative prestudy and four quantitative studies for item generation and content validity, scale refinement using exploratory factor analyses, scale confirmation using confirmatory factor analysis and scale validity using PLS-SEM is followed to develop the measure of brand pride.

The scale development procedure yields a two-dimensional measurement instrument for brand pride with affective and utilitarian dimensions. The proposed scale is reliable and has convergent, discriminant and nomological validity.

This study highlights the need to study brand pride by reviewing literature from both psychology and marketing. The definition and conceptualization of brand pride help uniquely identify and describe the phenomenon. By developing a scale to measure the construct of brand pride, this study will enable research into the phenomenon, its causes and effects and its significance for consumer–brand relationships.

Nanosized honeycomb-configured materials are used in modern technology, thermal science and chemical engineering due to their high ultra thermic relevance. This study aims to scrutinize the heat transmission features of magnetohydrodynamic (MHD) honeycomb-structured graphene nanofluid flow within two squeezed parallel plates under Joule dissipation and solar thermal radiation impacts.

Mass, energy and momentum preservation laws are assumed to find the mathematical model. A set of unified ordinary differential equations with nonlinear behavior is used to express the correlated partial differential equations of the established models, adopting a reasonable similarity adjustment. An approximate convergent numerical solution to these equations is evaluated by the shooting scheme with the Runge–Kutta–Fehlberg (RKF45) technique.

The impression of pertinent evolving parameters on the temperature, fluid velocity, entropy generation, skin friction coefficients and the heat transference rate is explored. Further, the significance of the irreversibility nature of heat transfer due to evolving flow parameters are evaluated. It is noted that the heat transference rate performance is improved due to the imposition of the allied magnetic field, Joule dissipation, heat absorption, squeezing and thermal buoyancy parameters. The entropy generation upsurges due to rising magnetic field strength while its intensification is declined by enhancing the porosity parameter.

The uniqueness of this research work is the numerical evaluation of MHD honeycomb-structured graphene nanofluid flow within two squeezed parallel plates under Joule dissipation and solar thermal radiation impacts. Furthermore, regression models are devised to forecast the correlation between the rate of thermal heat transmission and persistent flow parameters.