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Models for analyzing the impact of leadership and followership values on organizational outcomes
Followers have been the center of organizational focus in modern structure. The activation of followership could be a sign of successful leadership. Leaders must begin to understand the types of people they lead. Team members identify themselves as a unit and practically plan organizational development and progress to achieve similar strategies and objectives. The development of a leadermember exchange is based on characteristics of the working relationship as opposed to a personal or friendship relationship. Leaders create unity through demonstration of group-mindedness by making more references to the collective history, the collective identity and interests, and collective efficacy. The more leaders augment follower identification (through role modeling or group socialization), the more followers will likely experience higher feelings of ownership and responsibility. This paper is intended to characterize the types of followers that might exist in organizations and establish an integration of followers classification. 2026 selection and editorial matter, Mukesh Kumar Awasthi, Ashwani Kumar, Manoj Gupta; individual chapters, the contributors. -
Designing optimization frameworks for ICT-enabled e-leadership strategies
The rapid development of information and communication technology (ICT), especially, makes it easier for individuals to create, organize, as well as access information, which has significant effects on the skills required of leaders. The allocation of power and the emergence of connections in organizations might be impacted by new technologies. As a result, leadership is being placed in a new context in an information technology-enabled economy. It is crucial to consider how technological advancement and leadership interact to affect both the structure and outcomes of leadership, as well as how leadership itself may affect the adoption of cutting-edge information technology and its effects on organizations. In the internet era, leadership is undoubtedly different. As the world continues to change as a result of the apparent and astonishing advancements in computer and communications technology, it is imperative that we consider what has changed and what has stayed the same. The impact of the e-factor on leadership is one very significant setting for leadership. 2026 selection and editorial matter, Mukesh Kumar Awasthi, Ashwani Kumar, Manoj Gupta; individual chapters, the contributors. -
Exploring the Role of Artificial Intelligence in Educational Technology
AI is a dedicated field that addresses cognitive challenges often linked to human intelligence, including learning, problem-solving, and pattern recognition. This chapter investigates the evolving role of artificial intelligence (AI) in educational technology (EdTech). EdTech, short for EdTech, uses technology to support teaching and learning. EdTech firms employ advanced technology to offer personalized, experiential learning and online mentorship. With the rapid advancements in AI and its integration into various sectors, the educational landscape has witnessed significant transformations. This study explores the potential of AI as a powerful tool in enhancing teaching and learning experiences, improving educational outcomes, and addressing individual student needs. Employing the research synthesis methodology, this chapter provides a comprehensive overview of AIs role in EdTech, highlighting its capacity to revolutionize teaching and learning practices. Finally, the chapter concludes with an outlook on the future of AI in EdTech, discussing emerging trends and potential growth areas. 2026 selection and editorial matter, A.V. Senthil Kumar, Ankita Chaturvedi, Atul Bansal, and Rohaya Latip; individual chapters, the contributors. -
Unleashing the Potential: AIs Impact on Sustainable Finance in a Changing Global Economy
The world is becoming more conscious of sustainability, and the Finance sector is also influenced by this shift. Sustainable finance helps investors to make investment decisions by considering Environmental, Social and Governance (ESG) factors. There is a rapid change in this investment decision process. The main key reason for this change is the adoption of Artificial Intelligence (AI). The study pertains to understanding the impact of AI-based technologies on sustainable finance. AI-based technologies play a significant role in the transformation of sustainable finance by identifying quality data, doing better analysis and predicting ESG trends and investment risks. AI-driven technologies help investors to understand the disclosures on ESG practices, framework and policy mechanisms of the companies so that they make informed decisions and achieve Sustainable Development Goals (SDGs). However, use of AI-based technologies in sustainable finance is expected to be transparent, ethical, accountable and accurate. The study discussed the various applications of AI in achieving SDG goals, the use cases of AI and ESG investment and the relation of green finance and green economy. Few challenges in integrating AI and sustainability are also discussed in the chapter. 2026 selection and editorial matter, A.V. Senthil Kumar, Ankita Chaturvedi, Atul Bansal, and Rohaya Latip; individual chapters, the contributors. -
Future-Proofing Sustainable Urban Development: Harnessing Fuzzy Logic for Smart Cities
Urban development is the biggest challenge in this era of rapid urbanization. This chapter proposes a new approach to tackle this challenge by incorporating fuzzy logic in smart cities. Fuzzy logic is known for handling uncertainty and vagueness thus it is a way to navigate the complexities of urban planning and governance. Incorporating fuzzy logic in a smart city framework is an opportunity to improve decision-making, optimize resource allocation, and reduce the risks of urban development. 216This chapter explores many facets of fuzzy logic and sustainable urban development in a legal context. It starts by giving an overview of the urbanization and sustainability challenges and sets the stage for the discussion of fuzzy logic as a tool to address these challenges. The concept of smart sustainable cities is explained, including the principles and components that make up them. Fuzzy logic in smart cities is the central theme of this chapter. By explaining the concept of fuzzy logic and its applications in an urban context, the chapter shows how it can manage the uncertainties of the urban environment. It also investigates the legal implications of incorporating fuzzy logic in urban governance structures. Through regulatory frameworks and case studies, the chapter discusses legal issues and challenges of integrating fuzzy logic in decision-making. The chapter also looks into the need for adaptive governance structures to accommodate the dynamic nature of smart sustainable cities. It proposes ways to integrate fuzzy logic in existing legal frameworks and flexibility and adaptability in urban governance. From case studies to best practices, the chapter gives insights into the successful implementation of fuzzy logic in smart sustainable cities, which are useful for policymakers and urban planners. In summary, the chapter shows the potential of fuzzy logic to future-proof sustainable urban development in the legal sector. To promote interdisciplinary discussion and provide practical recommendations, it contributes to the discourse on using fuzzy logic for sustainable urban governance. This chapter is a trigger for new approaches to urbanization and sustainability in a legal context. 2025 Jenny Stanford Publishing Pte. Ltd. All rights reserved. -
A Comparative Study on Blockchain Architectures for Secure and Transparent Healthcare Systems
The research compares blockchain technologies in the healthcare industry, focusing on data security, decentralization, and transparency for managing data. Traditional healthcare systems face challenges, notably data breaches, inefficiencies in maintaining records, less interoperability, and issues regarding patient privacy. The solution with the use of the distributed nature of blockchain as an advantage is to provide a secure, decentralized space to store and manage medical data that is sensitive for creating a transparent, tamper-proof ledger accessible only by individuals or parties that are authorized. This paper highlights a comprehensive study of several blockchain architectures and applications in industry, such as electronic health records (EHRs), securing patient identity, tracking of drug supply chain, and secure medical data sharing. The approaches enhance data security and provide a transparent and trustworthy record of all data within a system. After analyzing numerous mechanisms and encryption approaches and combining blockchain with emerging technologies such as artificial intelligence (AI) and the Internet of Things (IoT), this chapter surveys blockchains prospect of enhancing healthcare efficiency while holding security and regulatory compliance. Likewise, the chapter discusses the restrictions of blockchain, including scalability, computational expenses, and lawful challenges, providing an understanding of forthcoming study trends and adoption methods for blockchain-based healthcare solutions. 2026 Anindya Nag, Md. Mehedi Hassan, Riya Sil and Asif Karim. -
Nanotechnology in the Food Industry
Food nanotechnology is a growing field that brings exciting new possibilities to the food industry, offering many opportunities for innovation. It can improve foods taste, health benefits, and nutritional value while leading to innovative products, packaging, and storage methods. Nanotechnology offers transformative potential in the food industry by designing nutrient delivery systems, nano-formulated agrochemicals, enhanced nutritional value, and novel bioactive encapsulation techniques. The food industry demands innovative technologies to maintain market leadership by producing fresh, authentic, convenient, and flavorful products. This chapter examines nanotechnologys role in food processing and packaging, emphasizing its potential for nanoscale control, personalized nutrition, enhanced product quality, and extended shelf life. It also provides insights into recent advances in industry-related R&D in food processing, focusing on innovations that improve efficiency and sustainability. This chapter further addresses food safety considerations and the regulatory measures necessary to manage health risks, such as nanoparticle accumulation and translocation in the body. 2026 selection and editorial matter, Reddicherla Umapathi, Naveen Kumar, and Rajat Singh; individual chapters, the contributors. -
Impact of green management on firm performance in the purview of AI and data analytics: A comprehensive review
Sustainability has evolved from being a goal to a fundamental practice in the business world. Organizational survival depends on its adaptability, and innovative practices are a dire necessity for businesses to stay afloat. From this perspective, data analytics and artificial intelligence highly influence business decisions. They are elemental, have a substantial impact, and hence need to be absorbed into each aspect of organizational management. Over the years, businesses embracing green management principles have witnessed a significant impact on overall organizational performance. This study aims to provide a broad-spectrum analysis of this discipline using bibliometric analysis conducted on publications in the Scopus database over the last three decades using VOSviewer software. Multiple aspects of the discipline are tested to provide comprehensive results. The subject, citations, contributions of authors, countries, and institutions, as well as the active sources of publication, are some of the disclosures in the current study. These aspects collectively reveal the positive relationship between the adaptation of green management and firm performance through the lens of data analytics and AI. It is necessary to understand the depth of previous and ongoing advancements to propose new postulates and ideologies, which is met in this chapter. 2026 Anand Patil, Swathi Shekar. All rights reserved. -
Exploring the intersection: Technological innovations and green management in enhancing sustainable organizational performance
Organizational changes in recent times have indicated a shift toward using innovative approaches to solve environmental concerns in both processes and products. This is in response to calls from stakeholders for a more comprehensive approach to business operations that puts profit, the environment, and people first-also known as the triple bottom line. In line with the resource-based view (RBV) framework, organizations can effectively address environmental challenges and meet social responsibilities. Embracing technologies such as AI, ML, and automation in their green practice operations unlocks new opportunities, new markets, and partnerships. This shift strengthens their reputations and goodwill among stakeholders. Although there is extant literature on green initiatives and organizational success, there is a dearth of thorough research that examines green management innovations and technological techniques in their entirety. This study attempts to close this gap by defining several aspects of green management and examining how green initiatives and technology affect future growth coupled with organizational performance and success. This study employs bibliometric analysis to conduct a systematic literature evaluation, focusing on a dataset (n = 1026) spanning the years 2005 to 2024. The results reveal (i) the two dominant clusters are, first, sustainability, sustainable development, innovations, digital technologies, and supply chain management, and, second, sustainability, performance assessment, and industrial performance, while a third cluster-sustainability, industrial performance, and innovation-reveals a growing interest among the research community; (ii) productivity and the impact of publications, authors, and countries prominently crusading on the theme; and (iii) thematic clusters that open the scope for further research. 2026 Shivakami Rajan, L.R. Niranjan. All rights reserved. -
Underlying Opportunities and Challenges of Digitalization in Gig Economy
There has been enormous growth in the gig sector across the world. The term gig refers to the short-term arrangement. For instance, an aspiring musician might celebrate after he obtains a gig contract or a painter may get a gig contract for some particular period of time. Both of them might get paid either a fixed fee or a fee based on the contract given. The gig economy has given rise to freelance workers, part-time workers, project-based workers, independent contractors, and contract workers. The gig economy has occupied a prominent place in the United States. According to the survey conducted by Intuit, nearly 40% of American workers are working as an independent contract worker in the year 2020. By means of the increase in the cost level and large masses seeking for jobs and livelihood, the concept of gig economy has opened doors for earning extra income through the gig work. The concept of digital platforms, greater digitalization, and accessibility to higher technology advancement such as the adoption of Artificial Intelligence is giving a boost to the concept of gig economy. There has been a growth in the acceptance of smartphones and the percentage of digitalization is an aiding factor permitting gig freelancers to offer numerous qualified services over the tech-based platform. Gig employees have the potential of signing any tasks in which they have a specific skill set. For example, a worker specialized in interior beautification on an ad-hoc basis for various customers as an alternative of undertaking a full-time inside designing work at a company. Advanced countries such as the United States were the initial adoption of the gig economy started due to advanced rates of digitalization, economic growth, and increase in the disposable income. All these enabling factors were responsible for foremost growing firms being initiated in the United States. Examples of firms are Uber, Airbnb, and Upwork. Nations such as the United States are presently a frontrunner in the international gig economy. On the contrary, emerging nations like India characterize a countless latent in terms of adoption of the gig economy. The reason behind the boost in gig economy is the growing supply of outworkers and lesser skilled labour force. With further knowledge diffusion and the upgrading in human resources and capital, India will nurture in the worldwide gig economy at a hastening pace. India can follow the United States gig economy by studying its trends and encourage the possible gig responsibilities. There are instances such as Ola and OYO operational in the Indian Economy. 2025 selection and editorial matter Alex Khang, Babasaheb Jadhav, Vugar Abdullayev Hajimahmud and Ipseeta Satpathy. -
Click, Connect, and Convert: Exploring the Impact of Social Media on Consumer Buying Behavior: A Comprehensive Review
Change is dynamic. If you dont change, the world will change you. This sentence perfectly fits into todays scenario of buying behavior where social media is one of the most powerful tools in determining the dynamic shopping habits of consumers in any sector. Social media plays different roles like an influencer, persuader, and convincer in todays era where the majority of the global population uses the Internet on daily basis. The factors within social media affecting buying behavior are word-of-mouth, website quality, perceived usage, perceived easiness, attitude, perception, price, consumer reviews, product recommendations, customer engagements, social communities, promotional tools (vouchers, movie tickets, gift samples). The data available is too large and generated daily because the number of online users increases on daily basis. This chapter intends to theoretically study the factors and how these factors influence different segments of customers in online buying behavior. The focus is to understand the powerful role of social media in influencing and motivating customers through different social media platforms like YouTube, Facebook, LinkedIn, Twitter, and Instagram. Secondary sources of data like recent research papers, books, and recent research studies have been considered for the study. Social media is highly influential in purchase decisions. The factors like customer engagements, product recommendations, reviews and opinions, social communities, contests, and free gifts play a major role in creating a positive perception and attitude toward the products thereby influencing and promoting purchases on social media. Marketers should advise companies to encourage users to post pictures along with the product/service on social media, creating awareness and building brand value in consumers minds. With the growing usage of the Internet, it is even estimated to grow exponentially. 2026 by Apple Academic Press, Inc. All rights reserved. -
Optimizing financial fraud detection models using genetic algorithms
In the contemporary financial environment, financial deception is a persistent challenge that results in significant economic losses annually. Using machine learning models to detect fraud has become an essential instrument for financial institutions to mitigate these risks. Nevertheless, the optimization of these models to achieve a balance between efficiency and accuracy continues to be a significant obstacle. In this chapter, the application of Genetic Algorithm (GA) as a potent optimization technique for improving financial fraud detection models is examined. Inspired by natural selection, GAs provide a unique way to addressing complicated optimization problems by iteratively improving a population of solutions. The chapter commences by providing a brief summary of financial detection and the limitations associated with conventional approaches. It then explores the fundamental concepts of GAs, including selection, crossover, mutation, and fitness evaluation, to provide a comprehensive understanding of how GAs may be used to improve fraud detection systems. In an exhaustive methodological section, we explore the actual use of GAs to optimize different model parameters, such as feature selection and hyperparameter tweaking. The analysis shows that GA-optimized models outperform standard approaches in terms of detection accuracy, false-positive rate, and computing efficiency. 2025 selection and editorial matter, Sulabh Bansal, Aprna Tripathi, Shilpa Srivastava and Prem Prakash Vuppuluri; individual chapters, the contributors. -
Particle swarm optimization- based support vector regression for predictions: Approach and applications
For centuries, people have drawn inspiration from nature, and there is always more to learn and discover. The Particle Swarm Optimization (PSO) algorithm, a stochastic optimization algorithm based on population and inspired by the intelligent collective behavior of certain animals like fish schools or flocks of birds, is one of the most well-known nature-inspired algorithms presented in this work. As more was known about the fundamentals of this methodology, researchers produced new iterations to satisfy varying needs, new applications in diverse domains, theoretical research on the effects of different parameters, and a multitude of algorithm variations. PSO-support vector regression (SVR) is one such variant of this algorithm. SVR is a kind of Support Vector Machine (SVM) that solves regression problems. It seeks to identify a function that diverges from the actual values observed by no more than a given margin. The main idea is to retain the error under a certain threshold. PSO optimizes SVR parameters, including regularization, epsilon, and kernel parameters. This combination takes advantage of the strengths of both approaches. In this chapter, we will discuss the importance of the PSO-SVR algorithm in predicting the outcomes of real-world applications classified as healthcare, environmental, industrial, commercial, smart city, and other broad applications. 2025 selection and editorial matter, Sulabh Bansal, Aprna Tripathi, Shilpa Srivastava and Prem Prakash Vuppuluri; individual chapters, the contributors. -
Real- coded genetic algorithm for optimal ordering and pricing in segmented market with freshness and price- dependent demand, advance payment, and trade credit
We study the inventory model of a product having demand affected by its freshness and selling price in the context of supply chains, freshness, and price-dependent demand, where the supplier is dominated, as is usually the case with producers of agri-based products. The product when received exhibits heterogeneous quality. The retailer subdivides the product into quality-dependent segments, which he sells simultaneously during the selling season at prices commensurate with the quality. The sizes of the segments are random variables. The supplier can get a partial advance payment from the dominant retailer by providing a discount on the partial advance with the proportion of partial payment as well as the epoch of partial payment chosen by the supplier. The retailer can, at times, choose the advance proportion to be paid, and the discounted price which we call the endogenous case but takes a loan for the advance payment from a financer, whom he repays with interest when a delayed payment period permitted by the supplier gets over. The retailer in turn gets some time before he can pay his remaining dues and pays the supplier a fraction of the cost price commensurate with the quality of the product. Lost sales shortages are considered for fresh items. The model is aimed at obtaining optimal values of ordering amount, selling price, and discounted selling prices for the various segments. It is also aimed to obtain advance proportion and the discount on advance payment for the endogenous case. Real-coded genetic algorithm (RCGA) and Hybrid RCGA have been used to obtain the optimal solutions for numerical examples and the results are compared. Finally, sensitivity analysis to evaluate the effects of changes in some parameter values has also been presented. 2025 selection and editorial matter, Sulabh Bansal, Aprna Tripathi, Shilpa Srivastava and Prem Prakash Vuppuluri; individual chapters, the contributors. -
Introduction to optimization: Techniques and applications in engineering
A key idea in computer science, engineering, economics, and mathematics is optimization, which seeks to identify the optimal option from a range of workable options. An overview of optimization, its importance, and its many uses are given in this chapter. It highlights various forms of optimization, such as linear, nonlinear, convex, and combinatorial optimization, and examines the fundamental concepts of optimization, such as objective functions, constraints, and viable regions. In addition to contemporary strategies such as evolutionary algorithms, machine learning-based optimization, and metaheuristic techniques like genetic algorithms and simulated annealing, the chapter explores few traditional optimization techniques. Real-world applications in banking, logistics, AI, and industrial process optimization are also covered. This chapter offers insights into issue formulation, solution approaches, and efficiency concerns, with a focus on both theoretical underpinnings and real-world applications. It also presents important optimization tools and software that are frequently used in both industry and academics. By the end of this chapter, readers will have a basic understanding of optimization concepts that will allow them to use these ideas to effectively tackle challenging issues. 2025 selection and editorial matter, Sulabh Bansal, Aprna Tripathi, Shilpa Srivastava and Prem Prakash Vuppuluri; individual chapters, the contributors. -
Time-Frequency Analysis of ECG Signal
Time-frequency analysis (TFA), especially well suited for biomedical applications, is a potent method for deciphering non-stationary signals, where frequency characteristics vary with time. These dynamic signals are too complex for conventional frequency analysis approaches, which calls for sophisticated techniques like the discrete wavelet transform, continuous wavelet transform, and short-time Fourier transform. This research focuses on the uses of TFA techniques in biomedical signal processing and how well they expose transitory phenomena and temporal patterns that are missed by conventional methods. In particular, we look at how TFA is applied to the analysis of electrocardiogram (ECG) signals. The chapter discusses baseline wander, notch filtering, and low-pass filtering as crucial pre-processing techniques for clean ECG readings. Furthermore, we present the symbolic aggregate approximation paradigm for effective data retrieval and storage. 2026 selection and editorial matter, Ganesh R. Naik. -
Skilful Leadership and Management: The Importance of Emotional Intelligence
Emotional intelligence (EI) has become more important in the study of organisational behaviour, particularly in relation to management and effective leadership. EI is the ability to identify, understand, and control ones own emotions as well as those of others. Those with high EI find it easier to navigate complex social interactions, build strong relationships, and resolve conflicts. EI is the ability to recognise, manage, and evaluate emotions. The ability to express ones emotions in a healthy way and to empathise with others is a sign of great emotional intelligence in a leader, and it will enhance both performance and workplace relationships. The study employed a range of machine learning (ML) methods, such as ANN, BRDT, Naive Bayes, and Random Forest, to predict EI based on behaviour credits. ML approaches have become more and more common. The results showed that the BRDT has the accuracy of 98.3 which is higher in all other machine learning models and gives better results. Seven behavioural attributes and seven additional individual attributes made up the prediction dataset. 2024 selection and editorial matter, Prof. (Dr.) Dorota Jelonek, Prof. (Dr.) Narendra Kumar, Prof. (Dr.) Mamta Chahar, Prof. (Dr.) Rusudan Kinkladze and Prof. (Dr.) Lilla Knop; individual chapters, the contributors.