Sendhilkumar Manoharan

The integration of artificial intelligence with human resource management has accelerated in a big way, fundamentally altering the organizational decision-making processes, and thereby posing unprecedented governance issues for the corporate directors and officers. This theoretical analysis explores the problem of traditional corporate governance structures and fiduciary obligations being challenged by algorithmic HRM systems. Drawing on recent scholarship from corporate governance, organizational theory, and information systems, the authors examine the development of algorithmic HRM as a field of governance that is critical and needs to be addressed at the board level. They synthesize existing research addressing questions of AI governance, fiduciary duties in algorithmic contexts, and organizational accountability mechanisms in order to propose an integrative framework for algorithmic stewarding. The analysis shows important shortcomings in current governance models, especially the lack of clarity around AI decision-making, the lack of centralised structures of accountability, and the insufficiency of traditional monitoring mechanisms. They present the notion of algorithmic stewardship, expanding the concept of fiduciary theory to non-human decision agents and suggesting specific obligations for directors and officers in monitoring AI-mediated HRM. The framework identifies key governance capabilities, such as AI literacy, audit mechanisms, and escalation protocols that enable effective oversight. This review adds to corporate governance theory by reconfiguring the board responsibilities in algorithmic organizations and suggests practical implications to regulators and practitioners, as well as researchers dealing with the intersections of AI, HRM and corporate accountability.

Introduction: Effective Leadership is the foundation of corporate success, influencing attitudes among employees, performance, and engagement. However, many corporate workplaces struggle with disengaged employees and low commitment levels due to ineffective leadership practices. The research investigates the impact of managerial approaches on commitment and involvement of employee in business settings, concentrating on transformative, transactional, and servant leadership, and their impact on affective commitment and behavioral engagement.Methodology: Data were acquired by using structured surveys from 400 individuals across business organizations in the information technology (IT), financial, and manufacturing sectors. The Hypothesis (H1-H8) was developed, and implemented in IBM SPSS statistic version 26. Data analysis included Pearson correlation, descriptive data, Structural Equation Modelling (SEM) and multiple regression analysis to explore both direct and indirect relationships among variables. Results: The findings showed transactional leadership (TL) had the most impact on employee commitment (EC) with a path coefficient (0.40) and employee engagement (EE) 0.43. The Servant leadership (SL) and transactional leadership (TSL) with moderate impact with path coefficients in SL (0.22, and 0.19) for EC and EE, in TSL (0.16) for EC, and (0.13) for EE. Conclusion: The research by SEM reveals that leadership Effectiveness is an important aspect in influencing how employees act, underlining the need to engage in leadership development efforts that enhance compassion, interaction, empowerment, and ethical behavior to increase motivation, commitment, and engagement.

This chapter investigates the PRISMA model for linking agility and sustainability into company strategy. It illustrates a noticeable shift of realizing the importance of finding a balance between long term resilience and adaptation. The former enables companies to fulfill the demands of a dynamic and transforming market while the latter sets out guidelines for their responsible and ethical operation. Although the two paradigms are very important, they are independent of each other most of the time; the relationship between them is the trade-off not the complementary.

This study provides an in-depth analysis of the digital transformation in the oil and gas industry, focusing on recent advancements, challenges, and implications. the research explores the impact of artificial intelligence (AI), big data analytics, automation, and digital integration on operational efficiency, safety, and decision-making. Despite significant technological advancements, the study identifies critical challenges including difficulties in integrating disparate systems, addressing human factors and training needs, and ensuring data security. High implementation costs and evolving regulatory requirements further complicate widespread adoption. The study highlights the need for cohesive frameworks to manage these challenges and maximize the benefits of digital technologies. By synthesizing current literature and identifying gaps, the research offers actionable insights for enhancing digital transformation strategies and fostering sustainable growth within the industry.

The transition from Beyond 5G (B5G) to 6G is a sea change in wireless communication technology, which is expected to upend industries such as transportation, healthcare, and manufacturing. By expanding upon the 5G network's current capabilities, B5G boosts mobile broadband, reduces power consumption, and maximizes spectrum usage. Advanced Internet of Things (IoT) devices and real-time applications are made possible by essential features like massive machine-type communications (mMTC) and ultra-low latency. The advent of 6G in the 2030s heralds revolutionary improvements such as intelligent surfaces, AI-driven network automation, and terahertz transmission frequencies. Thanks to this development, previously unimaginable speeds and bandwidth will be available, allowing for a proliferation of networked devices with latency reduced to microseconds. While resolving issues with present networks, these advancements will provide state-of-the-art solutions for smart cities and digital twins and enhance existing networks' capacities.

The advent of industrial automation has ushered in a transformative era, fundamentally altering operational dynamics and workforce structures across various sectors. Technologies such as robotics, artificial intelligence, and machine learning have enhanced efficiency, productivity, and precision in industries ranging from manufacturing and logistics to healthcare and finance. However, these advancements present unique challenges and opportunities for human resource management, necessitating a reevaluation of traditional practices. Human resource management must adapt to the integration of automated systems with human labor, focusing on talent acquisition, training, performance management, and employee engagement. As automation reshapes job roles and skills requirements, HR professionals must manage a hybrid workforce, address ethical considerations, and ensure data privacy and security. The goal is to equip HR professionals and industry stakeholders with the knowledge and tools to navigate the evolving landscape of industrial automation and its implications for the future workforce.

A crucial branch of science called climate modeling uses mathematical equations and computer simulations to study and forecast the Earth's climate sys- tem. The main elements of climate modeling, such as general circulation models (GCMs), data assimilation methods, and numerical formulations, are outlined in this paper. GCMs, which include grid point and spectral models, are effective instruments for examining the behavior of the climate. Four-Dimensional Data Assimilation (4D-Var) is one example of a data assimilation technique that in- corporates observational data into models to improve their correctness. Numeri cal methods, ocean dynamics, heat transport, radiative transfer, and atmospheric dynamics are all included in numerical formulations. The simulation of different climate processes is possible because to these mathematical representations. Fur thermore, the detection of precipitation patterns within climate modeling is using machine learning techniques like Random Forest more frequently. This paper highlights the importance of high-performance computing (HPC) in climate modeling, boosting efficiency and simulations, in the context of research technique. Advanced data assimilation and validation techniques are also examined, as well as the influence of high-resolution modeling on small-scale climatic processes. On HPC platforms, accessibility to climate modeling is addressed. It is shown how climate modeling crosses physics, mathematics, computer science, and engineering to be interdisciplinary. A comprehensive understanding of the Earth's intricate climate system gains from the integration of all its parts, from atmospheric dynamics to data assimilation. We explore the consequences of these research approaches, their contribution to enhancing climate prediction models, and the influence of various factors on climatic variables in the debate. Climate modeling becomes an essential tool for studying precipitation patterns and climate change, ultimately improving our comprehension of the complex cli- mate system on Earth.

In the relentless pursuit of outperforming competitors and meeting the evolving demands of an increasingly digitally adept audience, numerous companies are making substantial investments in cutting-edge technologies and embarking on transformative journeys towards digitalization. Recent studies have unveiled a significant trend that more than 80% of organizations are entrusting their IT teams with the responsibility of delivering a seamless, interconnected customer experience. While the judicious selection of technology remains undeniably pivotal, it is equally imperative to align the company’s culture with this new trajectory, ensuring that organizational teams are well-equipped to sustain the digital strategy for the long haul. At its essence, culture encapsulates the shared beliefs, values, customs, and practices of a group. Against this backdrop, this chapter endeavors to clarify the concepts of digital culture and digital transformation, underscore the pivotal importance of digital culture in the digital transformation process, provide insights into the methods for establishing and nurturing a digitalized organizational culture, showcase success stories within the realm of digital culture and digital transformation, delve into the nuances of designing and building digital culture, elaborate on the advantages of cultivating digital culture during the digital transformation journey, address the challenges that inevitably arise in this transformative process, and offer a glimpse into the future perspectives of digital culture within the ever-evolving landscape of digital transformation.

In the present era of information, social media has risen as the primary channel for engagement. Platforms like Facebook, Twitter, YouTube, and others possess the capability to bring about significant changes. They have been instrumental in activities ranging from the downfall of governments to the amplification of political campaigns, the facilitation of public protests, the enablement of social demonstrations, and the initiation of social campaigns. Moreover, these platforms promote communication, dialogue, and awareness within public forums, serving as immediate sources of news and information. Within the dynamic landscape of communication evolution, the interconnected population now enjoys unprecedented access to information, expanded opportunities for public discourse, and enhanced potential for collective action. Request access from your librarian to read this chapter's full text.

The internet of things characterizes a unified structure of internet-supported objects that can gather and send data through a wireless network with no intervention of humans. By this structure, the present business world is experiencing remarkable changes due to the irresistible potential derivable from the internet of things. This technology is already bringing significant changes across the industries, of which the digital marketing sector has the maximum benefits. This technology collects various data from the consumers through different forms of digital marketing platforms such as social media marketing, online marketing, electronic mail marketing, pay-per-click advertising. With these types of data, marketers can generate some meaningful insights, develop interactions with customers, communicate with sellers and customers, and can also forecast the behavior and lifestyle of the customers. With this backdrop, the chapter has made an attempt to explain the relevance of the internet of things in digital marketing.